Search results for: membership function
2168 Defuzzification of Periodic Membership Function on Circular Coordinates
Authors: Takashi Mitsuishi, Koji Saigusa
This paper presents circular polar coordinates transformation of periodic fuzzy membership function. The purpose is identification of domain of periodic membership functions in consequent part of IF-THEN rules. Proposed methods in this paper remove complicatedness concerning domain of periodic membership function from defuzzification in fuzzy approximate reasoning. Defuzzification on circular polar coordinates is also proposed.
Keywords: Defuzzification, periodic membership function, polar coordinates transformation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1938
2167 Design of Membership Ranges for Fuzzy Logic Control of Refrigeration Cycle Driven by a Variable Speed Compressor
Authors: Changho Han, Jaemin Lee, Li Hua, Seokkwon Jeong
Abstract: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.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 782
2166 A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets
Authors: O. Poleshchuk, E.Komarov
This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.
Keywords: Interval type-2 fuzzy sets, fuzzy regression, weighted interval.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2074
2165 Similarity Based Membership of Elements to Uncertain Concept in Information System
Authors: M. Kamel El-Sayed
The process of determining the degree of membership for an element to an uncertain concept has been found in many ways, using equivalence and symmetry relations in information systems. In the case of similarity, these methods did not take into account the degree of symmetry between elements. In this paper, we use a new definition for finding the membership based on the degree of symmetry. We provide an example to clarify the suggested methods and compare it with previous methods. This method opens the door to more accurate decisions in information systems.
Keywords: Information system, uncertain concept, membership function, similarity relation, degree of similarity.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 706
2164 Optimal Aggregate Production Planning with Fuzzy Data
Authors: Wen-Lung Huang, Shih-Pin Chen
Abstract:This paper investigates the optimization problem of multi-product aggregate production planning (APP) with fuzzy data. From a comprehensive viewpoint of conserving the fuzziness of input information, this paper proposes a method that can completely describe the membership function of the performance measure. The idea is based on the well-known Zadeh-s extension principle which plays an important role in fuzzy theory. In the proposed solution procedure, a pair of mathematical programs parameterized by possibility level a is formulated to calculate the bounds of the optimal performance measure at a . Then the membership function of the optimal performance measure is constructed by enumerating different values of a . Solutions obtained from the proposed method contain more information, and can offer more chance to achieve the feasible disaggregate plan. This is helpful to the decision-maker in practical applications.
Keywords: fuzzy data, aggregate production planning, membership function, parametric programmingProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1652
2163 Fuzzy Logic Based Maximum Power Point Tracking Designed for 10kW Solar Photovoltaic System with Different Membership Functions
Authors: S. Karthika, K. Velayutham, P. Rathika, D. Devaraj
The electric power supplied by a photovoltaic power generation systems depends on the solar irradiation and temperature. The PV system can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. The proposed MPPT controller is designed for 10kW solar PV system installed at Cape Institute of Technology. This paper presents the fuzzy logic based MPPT algorithm. However, instead of one type of membership function, different structures of fuzzy membership functions are used in the FLC design. The proposed controller is combined with the system and the results are obtained for each membership functions in Matlab/Simulink environment. Simulation results are decided that which membership function is more suitable for this system.
Keywords: MPPT, DC-DC Converter, Fuzzy logic controller, Photovoltaic (PV) system.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4119
2162 Study on Optimal Control Strategy of PM2.5 in Wuhan, China
Authors: Qiuling Xie, Shanliang Zhu, Zongdi Sun
In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.
Keywords: Grey relational degree, multiple linear regression, membership function, nonlinear programming.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1295
2161 Roll of Membership functions in Fuzzy Logic for Prediction of Shoot Length of Mustard Plant Based on Residual Analysis
Authors: Satyendra Nath Mandal, J. Pal Choudhury, Dilip De, S. R. Bhadra Chaudhuri
Abstract:The selection for plantation of a particular type of mustard plant depending on its productivity (pod yield) at the stage of maturity. The growth of mustard plant dependent on some parameters of that plant, these are shoot length, number of leaves, number of roots and roots length etc. As the plant is growing, some leaves may be fall down and some new leaves may come, so it can not gives the idea to develop the relationship with the seeds weight at mature stage of that plant. It is not possible to find the number of roots and root length of mustard plant at growing stage that will be harmful of this plant as roots goes deeper to deeper inside the land. Only the value of shoot length which increases in course of time can be measured at different time instances. Weather parameters are maximum and minimum humidity, rain fall, maximum and minimum temperature may effect the growth of the plant. The parameters of pollution, water, soil, distance and crop management may be dominant factors of growth of plant and its productivity. Considering all parameters, the growth of the plant is very uncertain, fuzzy environment can be considered for the prediction of shoot length at maturity of the plant. Fuzzification plays a greater role for fuzzification of data, which is based on certain membership functions. Here an effort has been made to fuzzify the original data based on gaussian function, triangular function, s-function, Trapezoidal and L –function. After that all fuzzified data are defuzzified to get normal form. Finally the error analysis (calculation of forecasting error and average error) indicates the membership function appropriate for fuzzification of data and use to predict the shoot length at maturity. The result is also verified using residual (Absolute Residual, Maximum of Absolute Residual, Mean Absolute Residual, Mean of Mean Absolute Residual, Median of Absolute Residual and Standard Deviation) analysis.
Keywords: Fuzzification, defuzzification, gaussian function, triangular function, trapezoidal function, s-function, , membership function, residual analysis.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2200
2160 I-Vague Groups
Authors: Zelalem Teshome Wale
Abstract:The notions of I-vague groups with membership and non-membership functions taking values in an involutary dually residuated lattice ordered semigroup are introduced which generalize the notions with truth values in a Boolean algebra as well as those usual vague sets whose membership and non-membership functions taking values in the unit interval [0, 1]. Moreover, various operations and properties are established.
Keywords: Involutary dually residuated lattice ordered semigroup, I-vague set and I-vague group.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1713
2159 Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR
Authors: Saeed Vaneshani, Hooshang Jazayeri-Rad
Abstract:Fuzzy logic control (FLC) systems have been tested in many technical and industrial applications as a useful modeling tool that can handle the uncertainties and nonlinearities of modern control systems. The main drawback of the FLC methodologies in the industrial environment is challenging for selecting the number of optimum tuning parameters. In this paper, a method has been proposed for finding the optimum membership functions of a fuzzy system using particle swarm optimization (PSO) algorithm. A synthetic algorithm combined from fuzzy logic control and PSO algorithm is used to design a controller for a continuous stirred tank reactor (CSTR) with the aim of achieving the accurate and acceptable desired results. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear CSTR system as a case study. It is clearly proved that the optimized membership functions (MFs) provided better performance than a fuzzy model for the same system, when the MFs were heuristically defined.
Keywords: continuous stirred tank reactor (CSTR), fuzzy logiccontrol (FLC), membership function(MF), particle swarmoptimization (PSO)Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3090
2158 Membership Surface and Arithmetic Operations of Imprecise Matrix
Authors: Dhruba Das
Abstract:In this paper, a method has been developed to construct the membership surfaces of row and column vectors and arithmetic operations of imprecise matrix. A matrix with imprecise elements would be called an imprecise matrix. The membership surface of imprecise vector has been already shown based on Randomness-Impreciseness Consistency Principle. The Randomness- Impreciseness Consistency Principle leads to defining a normal law of impreciseness using two different laws of randomness. In this paper, the author has shown row and column membership surfaces and arithmetic operations of imprecise matrix and demonstrated with the help of numerical example.
Keywords: Imprecise number, Imprecise vector, Membership surface, Imprecise matrix.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1334
2157 I-Vague Normal Groups
Authors: Zelalem Teshome Wale
Abstract:The notions of I-vague normal groups with membership and non-membership functions taking values in an involutary dually residuated lattice ordered semigroup are introduced which generalize the notions with truth values in a Boolean algebra as well as those usual vague sets whose membership and non-membership functions taking values in the unit interval [0, 1]. Various operations and properties are established.
Keywords: Involutary dually residuated lattice ordered semigroup, I-vague set, I-vague group and I-vague normal group.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1453
2156 Proposal of Additional Fuzzy Membership Functions in Smoothing Transition Autoregressive Models
Authors: Ε. Giovanis
Abstract:In this paper we present, propose and examine additional membership functions for the Smoothing Transition Autoregressive (STAR) models. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. Because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach, more fuzzy membership functions should be tested. Furthermore, fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation or genetic algorithm instead to nonlinear squares. We examine two macroeconomic variables of US economy, the inflation rate and the 6-monthly treasury bills interest rates.
Keywords: Forecast , Fuzzy membership functions, Smoothingtransition, Time-seriesProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1421
2155 Level of Concentration in Banking Markets and Length of EU Membership
Authors: Ivan Pavic, Fran Galetic, Tomislava Pavic Kramaric
Abstract:The purpose of this article is to analyze the degree of concentration in the banking market in EU member states as well as to determine the impact of the length of EU membership on the degree of concentration. In that sense several analysis were conducted, specifically, panel analysis, calculation of correlation coefficient and regression analysis of the impact of the length of EU membership on the degree of concentration. Panel analysis was conducted to determine whether there is a similar trend of concentration in three groups of countries - countries with a low, moderate and high level of concentration. The conducted panel analysis showed that in EU countries with a moderate level of concentration, the level of concentration decreases. The calculation of correlation showed that, to some extent, with other influential factors, the length of EU membership negatively affects the market concentration of the banking market. Using the regression analysis for investigation of the influence of the length of EU membership on the level of concentration in the banking sector in a particular country, the results reveal that there is a negative effect of the length in EU membership on market concentration, although it is not significantly influential variable.
Keywords: Banking sector, concentration, EUProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1616
2154 Factors Related to Being Good Membership Behavior in Organization of Personnel at Suan Sunandha Rajabhat University
Authors: Patchalaphon Seeladlao, Anocha Kimkong
The aims of this study were to compare the differences of being good membership behavior among faculties and staffs of Suan Sunandha Rajabhat University with different sex, age, income, education, marital status, and working period, and investigate the relationships between organizational commitment and being good membership behavior. The research methodology employed a questionnaire as a quantitative method. The respondents were 305 faculties and staffs of Suan Sunandha Rajabhat University. This research used Percentage, Mean, Standard Deviation, t-test, One-Way ANOVA Analysis of Variance, and Pearson’s Product Moment Correlation Coefficient in data analysis. The results showed that organizational commitment among faculties and staffs of Suan Sunandha Rajabhat University was at a high level. In addition, differences in sex, age, income, education, marital status, and working period revealed differences in being good membership behavior. The results also indicated that organizational commitment was significantly related to being good membership behavior.
Keywords: Being Good membership behavior, Organizational Commitment.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1669
2153 Dataset Analysis Using Membership-Deviation Graph
Authors: Itgel Bayarsaikhan, Jimin Lee, Sejong Oh
Abstract:Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification.
Keywords: feature, classification, machine learning algorithm.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1324
2152 Sensory Evaluation of the Selected Coffee Products Using Fuzzy Approach
Authors: M.A. Lazim, M. Suriani
Abstract:Knowing consumers' preferences and perceptions of the sensory evaluation of drink products are very significant to manufacturers and retailers alike. With no appropriate sensory analysis, there is a high risk of market disappointment. This paper aims to rank the selected coffee products and also to determine the best of quality attribute through sensory evaluation using fuzzy decision making model. Three products of coffee drinks were used for sensory evaluation. Data were collected from thirty judges at a hypermarket in Kuala Terengganu, Malaysia. The judges were asked to specify their sensory evaluation in linguistic terms of the quality attributes of colour, smell, taste and mouth feel for each product and also the weight of each quality attribute. Five fuzzy linguistic terms represent the quality attributes were introduced prior analysing. The judgment membership function and the weights were compared to rank the products and also to determine the best quality attribute. The product of Indoc was judged as the first in ranking and 'taste' as the best quality attribute. These implicate the importance of sensory evaluation in identifying consumers- preferences and also the competency of fuzzy approach in decision making.
Keywords: fuzzy decision making, fuzzy linguistic, membership function, sensory evaluation,Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2660
2151 Union Membership with Import Liberalization
Authors: Stephen B. Blumenfeld, Aaron Crawford, Andres G. Victorio
Abstract:New Zealand-s product markets experienced a surge in import competition beginning from the late 1970-s when its government began to promote a policy of more open markets. This study considers how the trade liberalization aspect of the policy may have influenced unionization and union-organizing success. For describing the trade liberalization, a model shows how the removal of import tariffs can lead to countervailing influences upon the union membership of a domestic firm. The evidence supports the prediction that union membership has been decreased rather than increased. In the context of debates concerning globalization, it can be said that the power of unions has been diminished.
Keywords: Imports, tariffs, unions, wages.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1344
2150 A Multi-objective Fuzzy Optimization Method of Resource Input Based on Genetic Algorithm
Abstract:With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people-s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory. And then, a fitness function is constructed for genetic algorithm. Finally, a numerical example is presented to show that the method compared with traditional single-objective optimization method is more practical and efficient.
Keywords: Fitness function, genetic algorithm, multi-objectivefuzzy optimization, satisfaction degree membership function.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1254
2149 Efficient Realization of an ADFE with a New Adaptive Algorithm
Authors: N. Praveen Kumar, Abhijit Mitra, C. Ardil
Abstract:Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.
Keywords: Decision feedback equalizer, Adaptive algorithm, Block based computation, Set membership filtering.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1562
2148 Adaptation of Iterative Methods to Solve Fuzzy Mathematical Programming Problems
Authors: Ricardo C. Silva, Luiza A. P. Cantao, Akebo Yamakami
Abstract:Based on the fuzzy set theory this work develops two adaptations of iterative methods that solve mathematical programming problems with uncertainties in the objective function and in the set of constraints. The first one uses the approach proposed by Zimmermann to fuzzy linear programming problems as a basis and the second one obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. We outline similarities between the two iterative methods studied. Selected examples from the literature are presented to validate the efficiency of the methods addressed.
Keywords: Fuzzy Theory, Nonlinear Optimization, Fuzzy Mathematics Programming.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1517
2147 Stock Price Forecast by Using Neuro-Fuzzy Inference System
Authors: Ebrahim Abbasi, Amir Abouec
Abstract:In this research, the researchers have managed to design a model to investigate the current trend of stock price of the "IRAN KHODRO corporation" at Tehran Stock Exchange by utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm Period, a Neuro-Fuzzy with two Triangular membership functions and four independent Variables including trade volume, Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also closing Price and Stock Price fluctuation as an dependent variable are selected as an optimal model. For the short-term Period, a neureo – fuzzy model with two triangular membership functions for the first quarter of a year, two trapezoidal membership functions for the Second quarter of a year, two Gaussian combination membership functions for the third quarter of a year and two trapezoidal membership functions for the fourth quarter of a year were selected as an optimal model for the stock price forecasting. In addition, three independent variables including trade volume, price to earning ratio, closing Stock Price and a dependent variable of stock price fluctuation were selected as an optimal model. The findings of the research demonstrate that the trend of stock price could be forecasted with the lower level of error.
Keywords: Stock Price forecast, membership functions, Adaptive Neuro-Fuzzy Inference System, trade volume, P/E, DPS.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2503
2146 Face Authentication for Access Control based on SVM using Class Characteristics
Authors: SeHun Lim, Sanghoon Kim, Sun-Tae Chung, Seongwon Cho
Abstract:Face authentication for access control is a face membership authentication which passes the person of the incoming face if he turns out to be one of an enrolled person based on face recognition or rejects if not. Face membership authentication belongs to the two class classification problem where SVM(Support Vector Machine) has been successfully applied and shows better performance compared to the conventional threshold-based classification. However, most of previous SVMs have been trained using image feature vectors extracted from face images of each class member(enrolled class/unenrolled class) so that they are not robust to variations in illuminations, poses, and facial expressions and much affected by changes in member configuration of the enrolled class In this paper, we propose an effective face membership authentication method based on SVM using class discriminating features which represent an incoming face image-s associability with each class distinctively. These class discriminating features are weakly related with image features so that they are less affected by variations in illuminations, poses and facial expression. Through experiments, it is shown that the proposed face membership authentication method performs better than the threshold rule-based or the conventional SVM-based authentication methods and is relatively less affected by changes in member size and membership.
Keywords: Face Authentication, Access control, member ship authentication, SVM.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1282
2145 Reliability Analysis of Press Unit using Vague Set
Authors: S. P. Sharma, Monica Rani
In conventional reliability assessment, the reliability data of system components are treated as crisp values. The collected data have some uncertainties due to errors by human beings/machines or any other sources. These uncertainty factors will limit the understanding of system component failure due to the reason of incomplete data. In these situations, we need to generalize classical methods to fuzzy environment for studying and analyzing the systems of interest. Fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. Essentially, in a Fuzzy Set (FS) each element is associated with a point-value selected from the unit interval [0, 1], which is termed as the grade of membership in the set. A Vague Set (VS), as well as an Intuitionistic Fuzzy Set (IFS), is a further generalization of an FS. Instead of using point-based membership as in FS, interval-based membership is used in VS. The interval-based membership in VS is more expressive in capturing vagueness of data. In the present paper, vague set theory coupled with conventional Lambda-Tau method is presented for reliability analysis of repairable systems. The methodology uses Petri nets (PN) to model the system instead of fault tree because it allows efficient simultaneous generation of minimal cuts and path sets. The presented method is illustrated with the press unit of the paper mill.
Keywords: Lambda -Tau methodology, Petri nets, repairable system, vague fuzzy set.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1418
2144 Fuzzy Logic Approach to Robust Regression Models of Uncertain Medical Categories
Authors: Arkady Bolotin
Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.
Keywords: Categorization, Uncertain medical categories, Binomial regression model, Fuzzy dependent variable, Robustness.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1428
2143 Optimization of Material Removal Rate in Electrical Discharge Machining Using Fuzzy Logic
Authors: Amit Kohli, Aashim Wadhwa, Tapan Virmani, Ujjwal Jain
Abstract:The objective of present work is to stimulate the machining of material by electrical discharge machining (EDM) to give effect of input parameters like discharge current (Ip), pulse on time (Ton), pulse off time (Toff) which can bring about changes in the output parameter, i.e. material removal rate. Experimental data was gathered from die sinking EDM process using copper electrode and Medium Carbon Steel (AISI 1040) as work-piece. The rules of membership function (MF) and the degree of closeness to the optimum value of the MMR are within the upper and lower range of the process parameters. It was found that proposed fuzzy model is in close agreement with the experimental results. By Intelligent, model based design and control of EDM process parameters in this study will help to enable dramatically decreased product and process development cycle times.
Keywords: Electrical discharge Machining (EDM), Fuzzy Logic, Material removal rate (MRR), Membership functions (MF).Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2587
2142 Induction Motor Speed Control Using Fuzzy Logic Controller
Authors: V. Chitra, R. S. Prabhakar
Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control are discussed in this paper. Soft computing technique – Fuzzy logic is applied in this paper for the speed control of induction motor to achieve maximum torque with minimum loss. The fuzzy logic controller is implemented using the Field Oriented Control technique as it provides better control of motor torque with high dynamic performance. The motor model is designed and membership functions are chosen according to the parameters of the motor model. The simulated design is tested using various tool boxes in MATLAB. The result concludes that the efficiency and reliability of the proposed speed controller is good.
Keywords: Induction motor, Field Oriented Control, Fuzzy logic controller, Maximum torque, Membership function.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3028
2141 An Extension of the Kratzel Function and Associated Inverse Gaussian Probability Distribution Occurring in Reliability Theory
Authors: R. K. Saxena, Ravi Saxena
In view of their importance and usefulness in reliability theory and probability distributions, several generalizations of the inverse Gaussian distribution and the Krtzel function are investigated in recent years. This has motivated the authors to introduce and study a new generalization of the inverse Gaussian distribution and the Krtzel function associated with a product of a Bessel function of the third kind )(zKQ and a Z - Fox-Wright generalized hyper geometric function introduced in this paper. The introduced function turns out to be a unified gamma-type function. Its incomplete forms are also discussed. Several properties of this gamma-type function are obtained. By means of this generalized function, we introduce a generalization of inverse Gaussian distribution, which is useful in reliability analysis, diffusion processes, and radio techniques etc. The inverse Gaussian distribution thus introduced also provides a generalization of the Krtzel function. Some basic statistical functions associated with this probability density function, such as moments, the Mellin transform, the moment generating function, the hazard rate function, and the mean residue life function are also obtained.KeywordsFox-Wright function, Inverse Gaussian distribution, Krtzel function & Bessel function of the third kind.
Keywords: Fox-Wright function, Inverse Gaussian distribution, Krtzel function & Bessel function of the third kind.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1602
2140 Noise Reduction in Image Sequences using an Effective Fuzzy Algorithm
Authors: Mahmoud Saeidi, Khadijeh Saeidi, Mahmoud Khaleghi
Abstract:In this paper, we propose a novel spatiotemporal fuzzy based algorithm for noise filtering of image sequences. Our proposed algorithm uses adaptive weights based on a triangular membership functions. In this algorithm median filter is used to suppress noise. Experimental results show when the images are corrupted by highdensity Salt and Pepper noise, our fuzzy based algorithm for noise filtering of image sequences, are much more effective in suppressing noise and preserving edges than the previously reported algorithms such as [1-7]. Indeed, assigned weights to noisy pixels are very adaptive so that they well make use of correlation of pixels. On the other hand, the motion estimation methods are erroneous and in highdensity noise they may degrade the filter performance. Therefore, our proposed fuzzy algorithm doesn-t need any estimation of motion trajectory. The proposed algorithm admissibly removes noise without having any knowledge of Salt and Pepper noise density.
Keywords: Image Sequences, Noise Reduction, fuzzy algorithm, triangular membership functionProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1751
2139 A Self Supervised Bi-directional Neural Network (BDSONN) Architecture for Object Extraction Guided by Beta Activation Function and Adaptive Fuzzy Context Sensitive Thresholding
Authors: Siddhartha Bhattacharyya, Paramartha Dutta, Ujjwal Maulik, Prashanta Kumar Nandi
Abstract:A multilayer self organizing neural neural network (MLSONN) architecture for binary object extraction, guided by a beta activation function and characterized by backpropagation of errors estimated from the linear indices of fuzziness of the network output states, is discussed. Since the MLSONN architecture is designed to operate in a single point fixed/uniform thresholding scenario, it does not take into cognizance the heterogeneity of image information in the extraction process. The performance of the MLSONN architecture with representative values of the threshold parameters of the beta activation function employed is also studied. A three layer bidirectional self organizing neural network (BDSONN) architecture comprising fully connected neurons, for the extraction of objects from a noisy background and capable of incorporating the underlying image context heterogeneity through variable and adaptive thresholding, is proposed in this article. The input layer of the network architecture represents the fuzzy membership information of the image scene to be extracted. The second layer (the intermediate layer) and the final layer (the output layer) of the network architecture deal with the self supervised object extraction task by bi-directional propagation of the network states. Each layer except the output layer is connected to the next layer following a neighborhood based topology. The output layer neurons are in turn, connected to the intermediate layer following similar topology, thus forming a counter-propagating architecture with the intermediate layer. The novelty of the proposed architecture is that the assignment/updating of the inter-layer connection weights are done using the relative fuzzy membership values at the constituent neurons in the different network layers. Another interesting feature of the network lies in the fact that the processing capabilities of the intermediate and the output layer neurons are guided by a beta activation function, which uses image context sensitive adaptive thresholding arising out of the fuzzy cardinality estimates of the different network neighborhood fuzzy subsets, rather than resorting to fixed and single point thresholding. An application of the proposed architecture for object extraction is demonstrated using a synthetic and a real life image. The extraction efficiency of the proposed network architecture is evaluated by a proposed system transfer index characteristic of the network.
Keywords: Beta activation function, fuzzy cardinality, multilayer self organizing neural network, object extraction,Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1445