Search results for: Interval Type-2 Fuzzy Logic

Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Lee Myung-Won, Kwak Keun-Chang

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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation

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Authors: Mariam Ismail, Kazeem Rufai, Jeremiah Balogun

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A study was conducted to apply fuzzy logic to the development of a monitoring model for child pornography based on associated risk factors, which can be used by forensic experts or integrated into forensic systems for the early detection of child pornographic activities. A number of methods were adopted in the study, which includes an extensive review of related works was done in order to identify the factors that are associated with child pornography following which they were validated by an expert sex psychologist and guidance counselor, and relevant data was collected. Fuzzy membership functions were used to fuzzify the associated variables identified alongside the risk of the occurrence of child pornography based on the inference rules that were provided by the experts consulted, and the fuzzy logic expert system was simulated using the Fuzzy Logic Toolbox available in the MATLAB Software Release 2016. The results of the study showed that there were 4 categories of risk factors required for assessing the risk of a suspect committing child pornography offenses. The results of the study showed that 2 and 3 triangular membership functions were used to formulate the risk factors based on the 2 and 3 number of labels assigned, respectively. The results of the study showed that 5 fuzzy logic models were formulated such that the first 4 was used to assess the impact of each category on child pornography while the last one takes the 4 outputs from the 4 fuzzy logic models as inputs required for assessing the risk of child pornography. The following conclusion was made; there were factors that were related to personal traits, social traits, history of child pornography crimes, and self-regulatory deficiency traits by the suspects required for the assessment of the risk of child pornography crimes committed by a suspect. Using the values of the identified risk factors selected for this study, the risk of child pornography can be easily assessed from their values in order to determine the likelihood of a suspect perpetuating the crime.

Keywords: fuzzy, membership functions, pornography, risk factors

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Authors: Nasser S. Shebka

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Fuzzy logic is used in complex adaptive systems where classical tools of representing knowledge are unproductive. Nevertheless, the incorporation of fuzzy logic, as it’s the case with all artificial intelligence tools, raised some inconsistencies and limitations in dealing with increased complexity systems and rules that apply to real-life situations and hinders the ability of the inference process of such systems, but it also faces some inconsistencies between inferences generated fuzzy rules of complex or imprecise knowledge-based systems. The use of fuzzy logic enhanced the capability of knowledge representation in such applications that requires fuzzy representation of truth values or similar multi-value constant parameters derived from multi-valued logic, which set the basis for the three t-norms and their based connectives which are actually continuous functions and any other continuous t-norm can be described as an ordinal sum of these three basic ones. However, some of the attempts to solve this dilemma were an alteration to fuzzy logic by means of non-monotonic logic, which is used to deal with the defeasible inference of expert systems reasoning, for example, to allow for inference retraction upon additional data. However, even the introduction of non-monotonic fuzzy reasoning faces a major issue of conflict resolution for which many principles were introduced, such as; the specificity principle and the weakest link principle. The aim of our work is to improve the logical representation and functional modelling of AI systems by presenting a method of resolving existing and potential rule conflicts by representing temporal modalities within defeasible inference rule-based systems. Our paper investigates the possibility of resolving fuzzy rules conflict in a non-monotonic fuzzy reasoning-based system by introducing temporal modalities and Kripke's general weak modal logic operators in order to expand its knowledge representation capabilities by means of flexibility in classifying newly generated rules, and hence, resolving potential conflicts between these fuzzy rules. We were able to address the aforementioned problem of our investigation by restructuring the inference process of the fuzzy rule-based system. This is achieved by using time-branching temporal logic in combination with restricted first-order logic quantifiers, as well as propositional logic to represent classical temporal modality operators. The resulting findings not only enhance the flexibility of complex rule-base systems inference process but contributes to the fundamental methods of building rule bases in such a manner that will allow for a wider range of applicable real-life situations derived from a quantitative and qualitative knowledge representational perspective.

Keywords: fuzzy rule-based systems, fuzzy tense inference, intelligent systems, temporal modalities

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Authors: Purnima Pandit

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Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis.

Keywords: fuzzy inference system, fuzzy logic, malaria disease, triangular fuzzy number

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Authors: Soroor K. H. Al-Khafaji, Manal Mohammad Abed

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The research aim of this paper is to evaluate the reliability of a complex engineering system and to design a fuzzy model for the reliability estimation. The designed model has been applied on Vegetable Oil Purification System (neutralization system) to help the specialist user based on the concept of FMEA (Failure Mode and Effect Analysis) to estimate the reliability of the repairable system at the vegetable oil industry. The fuzzy model has been used to predict the system reliability for a future time period, depending on a historical database for the two past years. The model can help to specify the system malfunctions and to predict its reliability during a future period in more accurate and reasonable results compared with the results obtained by the traditional method of reliability estimation.

Keywords: fuzzy logic, reliability, repairable systems, FMEA

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Authors: Mohammad Ali Alavidoost, Hossein Bozorgian

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Criterion-referenced tests (CRTs) are designed to measure student performance against a fixed set of predetermined criteria or learning standards. The reliability of such tests cannot be based on internal reliability. Threshold loss agreement is one way to calculate the reliability of CRTs. However, the selection of master and non-master in such agreement is determined by the threshold point. The problem is if the threshold point witnesses a minute change, the selection of master and non-master may have a drastic change, leading to the change in reliability results. Therefore, in this study, the Fuzzy logic approach is employed as a remedial procedure for data analysis to obviate the threshold point problem. Forty-one Iranian students were selected; the participants were all between 20 and 30 years old. A quantitative approach was used to address the research questions. In doing so, a quasi-experimental design was utilized since the selection of the participants was not randomized. Based on the Fuzzy logic approach, the threshold point would be more stable during the analysis, resulting in rather constant reliability results and more precise assessment.

Keywords: criterion-referenced tests, threshold loss agreement, threshold point, fuzzy logic approach

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Authors: Leticia Cervantes, Edith Garcia, Oscar Castillo

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At today, there are several control problems where the main objective is to obtain the best control in the study to decrease the error in the application. Many techniques can use to control these problems such as Neural Networks, PID control, Fuzzy Logic, Optimization techniques and many more. In this case, fuzzy logic with fuzzy system and an optimization technique are used to control the case of study. In this case, Ant Lion Optimization is used to optimize a fuzzy system to control the velocity of a simple treadmill. The main objective is to achieve the control of the velocity in the control problem using the ALO optimization. First, a simple fuzzy system was used to control the velocity of the treadmill it has two inputs (error and error change) and one output (desired speed), then results were obtained but to decrease the error the ALO optimization was developed to optimize the fuzzy system of the treadmill. Having the optimization, the simulation was performed, and results can prove that using the ALO optimization the control of the velocity was better than a conventional fuzzy system. This paper describes some basic concepts to help to understand the idea in this work, the methodology of the investigation (control problem, fuzzy system design, optimization), the results are presented and the optimization is used for the fuzzy system. A comparison between the simple fuzzy system and the optimized fuzzy systems are presented where it can be proving the optimization improved the control with good results the major findings of the study is that ALO optimization is a good alternative to improve the control because it helped to decrease the error in control applications even using any control technique to optimized, As a final statement is important to mentioned that the selected methodology was good because the control of the treadmill was improve using the optimization technique.

Keywords: ant lion optimization, control problem, fuzzy control, fuzzy system

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Authors: Kaddari Faiza, Mazari Benyounes, Mihoub Youcef, Safa Ahmed

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Active filtering of electric power has now become a mature technology for reactive power and harmonic compensation caused by the proliferation of power electronics devices used for industrial, commercial and residential purposes. The aim of this study is to enhance the power quality by improving the performances of shunt active power filter in harmonic mitigation to obtain sinusoidal source currents with very weak ripples. A power circuit configuration and control scheme for shunt active power filter are described with an improved method for harmonics compensation using self-tuning-filter for harmonics identification and fuzzy logic control to generate reference current. Simulation results (using MATLAB/SIMULINK) illustrates the compensation characteristics of the proposed control strategy. Analysis of these results proves the feasibility and effectiveness of this method to improve the power quality and also show the performances of fuzzy logic control which provides flexibility, high precision and fast response. The total harmonic distortion (THD %) for the simulations found to be within the recommended imposed IEEE 519-1992 harmonic standard.

Keywords: Active Powers Filter (APF), Self-Tuning-Filter (STF), fuzzy logic control, hysteresis-band control

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Authors: Changho Han, Jaemin Lee, Li Hua, Seokkwon Jeong

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Design of membership function ranges in fuzzy logic control (FLC) is presented for robust control of a variable speed refrigeration system (VSRS). The criterion values of the membership function ranges can be carried out from the static experimental data, and two different values are offered to compare control performance. Some simulations and real experiments for the VSRS were conducted to verify the validity of the designed membership functions. The experimental results showed good agreement with the simulation results, and the error change rate and its sampling time strongly affected the control performance at transient state of the VSRS.

Keywords: variable speed refrigeration system, fuzzy logic control, membership function range, control performance

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Authors: Ryan B. Escorial, Elmer A. Maravillas, Chris Jordan G. Aliac

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This study presents a small-scale water pumping system utilizing a fuzzy logic inference system attached to a renewable energy source. The fuzzy logic controller was designed and simulated in MATLAB fuzzy logic toolbox to examine the properties and characteristics of the input and output variables. The result of the simulation was implemented in a microcontroller, together with sensors, modules, and photovoltaic cells. The study used a grand rapid variety of lettuce, organic substrates, and foliar for observation of the capability of the device to irrigate crops. Two plant boxes intended for manual and automated irrigation were prepared with each box having 48 heads of lettuce. The observation of the system took 22-31 days, which is one harvest period of the crop. Results showed a 22.55% increase in agricultural productivity compared to manual irrigation. Aside from reducing human effort, and time, the smart irrigation system could help lessen some of the shortcomings of manual irrigations. It could facilitate the economical utilization of water, reducing consumption by 25%. The use of renewable energy could also help farmers reduce the cost of production by minimizing the use of diesel and gasoline.

Keywords: fuzzy logic, intelligent system, precision agriculture, renewable energy

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Amir Ghasem Norouzi, Mahdi Zowghi

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This study propose a framework based on the fuzzy T-Norms, T-conorm, a novel operator, and multi-expert approach to help organizations build awareness of the critical influential factors on the success of knowledge management (KM) implementation, analysis the failure of knowledge management. This study considers the complex uncertainty concept that is in knowledge management implementing capability (KMIC) and it is used by fuzzy logic for this reason. The contribution of our paper is shown with an empirical study in a nonprofit educational organization evaluation.

Keywords: fuzzy logic, knowledge management, multi expert analysis, consensus oriented average operator

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Authors: Hazim Abdulsada

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The inspection of underneath vehicle system has been given significant attention by governments after the threat of terrorism become more prevalent. New technologies such as mobile robots and computer vision are led to have more secure environment. This paper proposed that a mobile robot like Aria robot can be used to search and inspect the bombs under parking a lot vehicle. This robot is using fuzzy logic and subsumption algorithms to control the robot that movies underneath the vehicle. An OpenCV library and laser Hokuyo are added to Aria robot to complete the experiment for under vehicle inspection. This experiment was conducted at the indoor environment to demonstrate the efficiency of our methods to search objects and control the robot movements under vehicle. We got excellent results not only by controlling the robot movement but also inspecting object by the robot camera at same time. This success allowed us to know the requirement to construct a new cost effective robot with more functionality.

Keywords: fuzzy logic, mobile robots, Opencv, subsumption, under vehicle inspection

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Authors: Dejan Tanikić, Jelena Đoković, Saša Kalinović, Miodrag Manić, Saša Ranđelović

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This paper deals with measuring and modelling of the quality of the machined surface of the metal machining process. The average surface roughness (R_a) which represents the quality of the machined part was measured during the dry turning of the AISI 4140 steel. A large number of factors with the unknown relations among them influences this parameter, and that is why mathematical modelling is extremely complicated. Different values of cutting speed, feed rate, depth of cut (cutting regime) and workpiece hardness causes different surface roughness values. Modelling with soft computing techniques may be very useful in such cases. This paper presents the usage of the fuzzy logic-based system for determining metal machining process parameter in order to find the proper values of cutting regimes.

Keywords: fuzzy logic, metal machining, process modeling, surface roughness

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: Ikbal Eski, Turan Gürgenç

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After nearly more than a century of research and development, internal combustion engines have become almost perfect. Along with such improvement in internal combustion engines, automotive manufacturers are conducting research on design of alternative fuel vehicles. Nevertheless an ideal interim solution is to increase overall efficiency of internal combustion vehicles. A potential solution to achieve that is using continuously variable transmission system which, despite being an old idea, has recently become a hope for automotive manufacturers. CVT system, by continuously varying speed ratio, raises vehicle efficiency. In this study, fuzzy logic controller is used in speed ratio control of pulley based CVT system.

Keywords: continuously variable transmission system, variator, speed ratio, fuzzy logic

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Authors: Andrés C. Cuervo Pinilla, Christian G. Quintero M., Chinthaka Premachandra

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This paper considers people’s driving skills diagnosis under real driving conditions. In that sense, this research presents an approach that uses GPS signals which have a direct correlation with driving maneuvers. Besides, it is presented a novel expert-driving-criteria approximation using fuzzy logic which seeks to analyze GPS signals in order to issue an intelligent driving diagnosis. Based on above, this works presents in the first section the intelligent driving diagnosis system approach in terms of its own characteristics properties, explaining in detail significant considerations about how an expert-driving-criteria approximation must be developed. In the next section, the implementation of our developed system based on the proposed fuzzy logic approach is explained. Here, a proposed set of rules which corresponds to a quantitative abstraction of some traffics laws and driving secure techniques seeking to approach an expert-driving- criteria approximation is presented. Experimental testing has been performed in real driving conditions. The testing results show that the intelligent driving diagnosis system qualifies driver’s performance quantitatively with a high degree of reliability.

Keywords: driver support systems, intelligent transportation systems, fuzzy logic, real time data processing

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Authors: Takashi Mitsuishi

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Fuzzy logic has gained acceptance in the recent years in the fields of social sciences and humanities such as psychology and linguistics because it can manage the fuzziness of words and human subjectivity in a logical manner. However, the major field of application of the fuzzy logic is control engineering as it is a part of the set theory and mathematical logic. Mamdani method, which is the most popular technique for approximate reasoning in the field of fuzzy control, is one of the ways to numerically represent the control afforded by human language and sensitivity and has been applied in various practical control plants. Fuzzy logic has been gradually developing as an artificial intelligence in different applications such as neural networks, expert systems, and operations research. The objects of inference vary for different application fields. Some of these include time, angle, color, symptom and medical condition whose fuzzy membership function is a periodic function. In the defuzzification stage, the domain of the membership function should be unique to obtain uniqueness its defuzzified value. However, if the domain of the periodic membership function is determined as unique, an unintuitive defuzzified value may be obtained as the inference result using the center of gravity method. Therefore, the authors propose a method of circular-polar-coordinates transformation and defuzzification of the periodic membership functions in this study. The transformation to circular polar coordinates simplifies the domain of the periodic membership function. Defuzzified value in circular polar coordinates is an argument. Furthermore, it is required that the argument is calculated from a closed plane figure which is a periodic membership function on the circular polar coordinates. If the closed plane figure is continuous with the continuity of the membership function, a significant amount of computation is required. Therefore, to simplify the practice example and significantly reduce the computational complexity, we have discretized the continuous interval and the membership function in this study. In this study, the following three methods are proposed to decide the argument from the discrete polygon which the continuous plane figure is transformed into. The first method provides an argument of a straight line passing through the origin and through the coordinate of the arithmetic mean of each coordinate of the polygon (physical center of gravity). The second one provides an argument of a straight line passing through the origin and the coordinate of the geometric center of gravity of the polygon. The third one provides an argument of a straight line passing through the origin bisecting the perimeter of the polygon (or the closed continuous plane figure).

Keywords: defuzzification, fuzzy membership function, periodic function, polar coordinates transformation

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Authors: Dib Djalel, Mordjaoui Mourad

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The current trend towards the exploitation of various renewable energy resources has become indispensable, so it is important to improve the efficiency and reliability of the GPV photovoltaic systems. Maximum Power Point Tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions. This paper presents a new fuzzy logic control based MPPT algorithm for solar panel. The solar panel is modeled and analyzed in Matlab/Simulink. The Solar panel can produce maximum power at a particular operating point called Maximum Power Point(MPP). To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus, to extract maximum available power from a PV module, MPPT algorithms are implemented and Perturb and Observe (P&O) MPPT and fuzzy logic control FLC, MPPT are developed and compared. Simulation results show the effectiveness of the fuzzy control technique to produce a more stable power.

Keywords: MPPT, photovoltaic panel, fuzzy logic control, modeling, solar power

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Authors: Yog Raj Sood, Rajnish Shrivastava, Anchal Wadhwa

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Power transformer is one of the electrical equipment that has a central and critical role in the power system. In order to avoid power transformer failure, information system that provides the transformer condition is needed. This paper presents an information system to know the exact situations prevailing within the transformer by declaring its health index. Health index of a transformer is decided by considering several diagnostic tools. The current work deals with UV-Vis, IFT, FP, BDV and Water Content. UV/VIS results have been pre-accessed using separate FL controller for concluding with the Furan contents. It is broadly accepted that the life of a power transformer is the life of the oil/ paper insulating system. The method relies on the use of furan analysis (insulation paper), and other oil analysis results as a means to declare health index. Fuzzy logic system is used to develop the information system. The testing is done on 5 samples of oil of transformers of rating 132/66 KV to obtain the results and results are analyzed using fuzzy logic model.

Keywords: interfacial tension analyzer (ift), flash point (fp), furfuraldehyde (fal), health index

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Authors: Nafiseh Mollaei, Reihaneh Kardehi Moghaddam

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In case of Diabetes Mellitus the controlling of insulin is very difficult. This illness is an incurable disease affecting millions of people worldwide. Glucose is a sugar which provides energy to the cells. Insulin is a hormone which supports the absorption of glucose. Fuzzy control strategy is attractive for glucose control because it mimics the first and second phase responses that the pancreas beta cells use to control glucose. We propose two control algorithms a type-1 fuzzy controller and an interval type-2 fuzzy method for the insulin infusion. The closed loop system has been simulated for different patients with different parameters, in present of the food intake disturbance and it has been shown that the blood glucose concentrations at a normoglycemic level of 110 mg/dl in the reasonable amount of time. This paper deals with type 1 diabetes as a nonlinear model, which has been simulated in MATLAB-SIMULINK environment. The novel model, termed the Augmented Minimal Model is used in the simulations. There are some uncertainties in this model due to factors such as blood glucose, daily meals or sudden stress. In addition to eliminate the effects of uncertainty, different control methods may be utilized. In this article, fuzzy controller performance were assessed in terms of its ability to track a normoglycemic set point (110 mg/dl) in response to a [0-10] g meal disturbance. Finally, the development reported in this paper is supposed to simplify the insulin delivery, so increasing the quality of life of the patient.

Keywords: interval type-2, fuzzy controller, minimal augmented model, uncertainty

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Authors: Ekpeti Bukola Grace, Mahmoudi Sabar Esmail, Chaer Issa

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Global energy consumption has been increasing steadily over the last half - century, and this trend is projected to continue. As energy demand rises in many countries throughout the world due to population growth, natural ventilation in buildings has been identified as a viable option for lowering these demands, saving costs, and also lowering CO2 emissions. However, natural ventilation is driven by forces that are generally unpredictable in nature thus, it is important to manage the resulting airflow in order to maintain pleasant indoor conditions, making it a complex system that necessitates specific control approaches. The effective application of fuzzy logic technique amidst other intelligent systems is one of the best ways to bridge this gap, as its control dynamics relates more to human reasoning and linguistic descriptions. This article reviewed existing literature and presented practical solutions by applying fuzzy logic control with optimized techniques, selected input parameters, and expert rules to design a more effective control system. The control monitors used indoor temperature, outdoor temperature, carbon-dioxide levels, wind velocity, and rain as input variables to the system, while the output variable remains the control of window opening. This is achieved through the use of fuzzy logic control tool box in MATLAB and running simulations on SIMULINK to validate the effectiveness of the proposed system. Comparison analysis model via simulation is carried out, and with the data obtained, an improvement in control actions and energy savings was recorded.

Keywords: fuzzy logic, intelligent control systems, natural ventilation, optimization

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Indrajit Ghosh, Jaydip Roy

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Literacy rate is an important indicator for measurement of human development. But this is not a good one to capture the qualitative dimension of educational attainment of an individual or a society. The overall educational level of an area is an important issue beyond the literacy rate. The overall educational level can be thought of as an outcome of the educational levels of individuals. But there is no well-defined algorithm and mathematical model available to measure the overall educational level of an area. A heuristic approach based on accumulated experience of experts is effective one. It is evident that fuzzy logic offers a natural and convenient framework in modeling various concepts in social science domain. This work suggests the implementation of fuzzy logic to develop a mathematical model for measurement of educational attainment of an area in terms of Education Index. The contribution of the study is two folds: conceptualization of “Education Profile” and proposing a new mathematical model to measure educational attainment in terms of “Education Index”.

Keywords: education index, education profile, fuzzy logic, literacy

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Authors: S. Bouharati, F. Allag, M. Belmahdi, M. Bounechada

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In terms of ecology forecast effects of desertification, the purpose of this study is to develop a predictive model of growth and adaptation of species in arid environment and bioclimatic conditions. The impact of climate change and the desertification phenomena is the result of combined effects in magnitude and frequency of these phenomena. Like the data involved in the phytopathogenic process and bacteria growth in arid soil occur in an uncertain environment because of their complexity, it becomes necessary to have a suitable methodology for the analysis of these variables. The basic principles of fuzzy logic those are perfectly suited to this process. As input variables, we consider the physical parameters, soil type, bacteria nature, and plant species concerned. The result output variable is the adaptability of the species expressed by the growth rate or extinction. As a conclusion, we prevent the possible strategies for adaptation, with or without shifting areas of plantation and nature adequate vegetation.

Keywords: climate changes, dry soil, phytopathogenicity, predictive model, fuzzy logic

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Authors: A. M. Benomair, M. O. Tokhi

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This paper presents the investigation on a system model for the stabilization of a Magnetic Levitation System (Maglev’s). The magnetic levitation system is a challenging nonlinear mechatronic system in which an electromagnetic force is required to suspend an object (metal sphere) in air space. The electromagnetic force is very sensitive to the noise which can create acceleration forces on the metal sphere, causing the sphere to move into the unbalanced region. Maglev’s give the contribution in industry and this system has reduce the power consumption, has increase the power efficiency and reduce the cost maintenance. The common applications for Maglev’s Power Generation (e.g. wind turbine), Maglev’s trains and Medical Device (e.g. Magnetically suspended Artificial Heart Pump). This paper presents the comparison between dynamic response and robust characteristic for both conventional PD and Fuzzy PD controller. The main contribution of this paper is the proof of fuzzy PD type stabilization and robustness. By use of a method to tune the scaling factors of the linear PD type fuzzy controller from an equivalent tuned conventional PD.

Keywords: magnetic levitation system, PD controller, Fuzzy Logic Control, Fuzzy PD

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Authors: Abhimanyu Kumar, Chirag Gupta

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This work presents a comprehensive study on the employment of Model Predictive Control (MPC) for a three-phase voltage-source inverter to regulate the output voltage efficiently. The inverter is modeled via the Clarke Transformation, considering a scenario where the load is unknown. An LC filter model is developed, demonstrating its efficacy in Total Harmonic Distortion (THD) reduction. The system, when implemented with fault-tolerant multilevel inverter topologies, ensures reliable operation even under fault conditions, a requirement that is paramount with the increasing dependence on renewable energy sources. The research also integrates a Fuzzy Logic based fault tolerance system which identifies and manages faults, ensuring consistent inverter performance. The efficacy of the proposed methodology is substantiated through rigorous simulations and comparative results, shedding light on the voltage prediction efficiency and the robustness of the model even under fault conditions.

Keywords: total harmonic distortion, fuzzy logic, renewable energy sources, MLI

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Authors: Ali Koam, Ismail Ibedou, S. E. Abbas

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In this paper, it is introduced the notion of r-fuzzy ideal separation axioms Tᵢi = 0; 1; 2 based on a fuzzy ideal I on a fuzzy topological space (X; τ). An r-fuzzy ideal connectedness related to the fuzzy ideal I is introduced which has relations with a previous r-fuzzy fuzzy connectedness. An r-fuzzy ideal compactness related to Ι is introduced which has also relations with many other types of fuzzy compactness.

Keywords: fuzzy ideal, fuzzy separation axioms, fuzzy compactness, fuzzy connectedness

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Authors: Bouharati Lokman, Bouharati Imen, Bouharati Khaoula, Bouharati Oussama, Bouharati Saddek

Abstract:

Ionizing radiation exposure is an established cancer risk factor. Compared to other common environmental carcinogens, it is relatively easy to determine organ-specific radiation dose and, as a result, radiation dose-response relationships tend to be highly quantified. Nevertheless, there can be considerable uncertainty about questions of radiation-related cancer risk as they apply to risk protection and public policy, and the interpretations of interested parties can differ from one person to another. Examples of tools used in the analysis of the risk of developing cancer due to radiation are characterized by uncertainty. These uncertainties are related to the history of exposure and different assumptions involved in the calculation. We believe that the results of statistical calculations are characterized by uncertainty and imprecision. Having regard to the physiological variation from one person to another. In this study, we develop a tool based on fuzzy logic inference. As fuzzy logic deals with imprecise and uncertain, its application in this area is adequate. We propose a fuzzy system with three input variables (age, sex and body attainable cancer). The output variable expresses the risk of infringement rate of each organ. A base rule is established from recorded actual data. After successful simulation, this will instantly predict the risk of infringement rate of each body following chronic exposure to 0.1 Gy.

Keywords: radiation exposure, cancer, modeling, fuzzy logic

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