Search results for: Fuzzy Weighted Input Estimation Method

Authors: Tahseen A. Jilani, S. M. Aqil Burney, C. Ardil

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In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.

Keywords: Average forecasting error rate (AFER), Fuzziness offuzzy sets Fuzzy, If-Then rules, Multivariate fuzzy time series.

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Authors: Chen-Yuan Chen, Wan-I Lee, Yi-Chaio Sui, Cheng-Wu Chen

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This study focuses on the development of triangular fuzzy numbers, the revising of triangular fuzzy numbers, and the constructing of a HCFN (half-circle fuzzy number) model which can be utilized to perform more plural operations. They are further transformed for trigonometric functions and polar coordinates. From half-circle fuzzy numbers we can conceive cylindrical fuzzy numbers, which work better in algebraic operations. An example of fuzzy control is given in a simulation to show the applicability of the proposed half-circle fuzzy numbers.

Keywords: triangular fuzzy number, half-circle fuzzy numbers, predictions, polar coordinates, Lyapunov method

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Authors: Wen-Lung Huang, Shih-Pin Chen

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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 programming

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Authors: P.-W. Tsai, J.-W. Chen, C.-W. Chen, C.-Y. Chen

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In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: Half-circle fuzzy numbers, predictions, swarm intelligence, Lyapunov method.

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Authors: Michal Polanský, C. Ardil

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This paper shows a new method for design of fuzzy observers for Takagi-Sugeno systems. The method is based on Linear matrix inequalities (LMIs) and it allows to insert H constraint into the design procedure. The speed of estimation can tuned be specification of a decay rate of the observer closed loop system. We discuss here also the influence of parametric uncertainties at the output control system stability.

Keywords: H norm, Linear Matrix Inequalities, Observers, Takagi-Sugeno Systems, Parallel Distributed Compensation

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Authors: J. Mahanta, P.K. Das

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A new class of fuzzy closed sets, namely fuzzy weakly closed set in a fuzzy topological space is introduced and it is established that this class of fuzzy closed sets lies between fuzzy closed sets and fuzzy generalized closed sets. Alongwith the study of fundamental results of such closed sets, we define and characterize fuzzy weakly compact space and fuzzy weakly closed space.

Keywords: Fuzzy weakly-closed set, fuzzy weakly-closed space, fuzzy weakly-compactness, MSC: 54A40, 54D30.

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Authors: Z. Masoumi, B. Moaveni

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This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Structural health monitoring, Kalman filter, Least square estimation, structural system identification.

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Authors: Shu-Xin Miao

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In this paper, we present an efficient numerical algorithm, namely block homotopy perturbation method, for solving fuzzy linear systems based on homotopy perturbation method. Some numerical examples are given to show the efficiency of the algorithm.

Keywords: Homotopy perturbation method, fuzzy linear systems, block linear system, fuzzy solution, embedding parameter.

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Authors: Aye Aye Mon

Abstract:

The application of a simple microcontroller to deal with a three variable input and a single output fuzzy logic controller, with Proportional – Integral – Derivative (PID) response control built-in has been tested for an automatic voltage regulator. The fuzzifiers are based on fixed range of the variables of output voltage. The control output is used to control the wiper motor of the auto transformer to adjust the voltage, using fuzzy logic principles, so that the voltage is stabilized. In this report, the author will demonstrate how fuzzy logic might provide elegant and efficient solutions in the design of multivariable control based on experimental results rather than on mathematical models.

Keywords: Fuzzy logic system, PID Controller, control systems, controlled A V R

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

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Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: Surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations.

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Authors: C. Ardil

Abstract:

The purpose of this paper is to present fuzzy TOPSIS in an entropic fuzzy environment. Due to the ambiguous concepts often represented in decision data, exact values are insufficient to model real-life situations. In this paper, the rating of each alternative is defined in fuzzy linguistic terms, which can be expressed with triangular fuzzy numbers. The weight of each criterion is then derived from the decision matrix using the entropy weighting method. Next, a vertex method is proposed to calculate the distance between two triangular fuzzy numbers. According to the TOPSIS concept, a closeness coefficient is defined to determine the ranking order of all alternatives by simultaneously calculating the distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). Finally, an illustrative example of selecting stealth fighter aircraft is shown at the end of this article to highlight the procedure of the proposed method. Correlation analysis and validation analysis using TOPSIS, WSM, and WPM methods were performed to compare the ranking order of the alternatives.

Keywords: stealth fighter aircraft selection, fuzzy uncertainty theory (FUT), fuzzy entropic decision (FED), fuzzy linguistic variables, triangular fuzzy numbers, multiple criteria decision making analysis, MCDMA, TOPSIS, WSM, WPM

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Authors: A. Morsli, A.Tlemçani, N. Ould Cherchali, M. S. Boucherit

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This article presents two methods for the compensation of harmonics generated by a nonlinear load. The first is the classic method P-Q. The second is the controller by modern method of artificial intelligence specifically fuzzy logic. Both methods are applied to a shunt Active Power Filter (sAPF) based on a three-phase voltage converter at five levels NPC topology. In calculating the harmonic currents of reference, we use the algorithm P-Q and pulse generation, we use the intersective PWM. For flexibility and dynamics, we use fuzzy logic. The results give us clear that the rate of Harmonic Distortion issued by fuzzy logic is better than P-Q.

Keywords: Fuzzy logic controller, P-Q method, Pulse Width Modulation (PWM), shunt Active Power Filter (sAPF), Total Harmonic Distortion (THD).

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Authors: Mahmoud Saeidi, Khadijeh Saeidi, Mahmoud Khaleghi

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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 function

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Authors: Mahmoud R. Shaghaghian

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Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.

Keywords: Dissolved oxygen, Au Sable, fuzzy logic modeling, Wang Mendel.

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Authors: Binu Thomas, Raju G., Sonam Wangmo

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In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program.

Keywords: Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.

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Authors: Azhar Hussain, Kashif Shahzad, Chunming Tang

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this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.

Keywords: Defuzzification, Fuzzy similarity approach, lane cropping, Maximum a Posterior Probability (MAP) estimator, Speed estimation

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Authors: Huei-Fu Lu

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This paper is to develop a fuzzy net present value (FNPV) method by taking vague cash flow and imprecise required rate of return into account for evaluating the value of the Build-Operate-Transfer (BOT) sport facilities. In order to clearly manifest a more realistic capital budgeting model based on the classical net present value (NPV) method, some uncertain financial elements in NPV formula will be fuzzified as triangular fuzzy numbers. Through the conscientious manipulation of fuzzy set theory, we will find that the proposed FNPV model is a more explicit extension of classical (crisp) model and could be more practicable for the financial managers to capture the essence of capital budgeting of sport facilities than non-fuzzy model.

Keywords: Fuzzy sets; Capital budgeting, Sport facility, Net present value (NPV), Build-Operate-Transfer (BOT) scheme

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Authors: Delaram Jarchi, Reza Boostani

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Linear Discrimination Analysis (LDA) is a linear solution for classification of two classes. In this paper, we propose a variant LDA method for multi-class problem which redefines the between class and within class scatter matrices by incorporating a weight function into each of them. The aim is to separate classes as much as possible in a situation that one class is well separated from other classes, incidentally, that class must have a little influence on classification. It has been suggested to alleviate influence of classes that are well separated by adding a weight into between class scatter matrix and within class scatter matrix. To obtain a simple and effective weight function, ordinary LDA between every two classes has been used in order to find Fisher discrimination value and passed it as an input into two weight functions and redefined between class and within class scatter matrices. Experimental results showed that our new LDA method improved classification rate, on glass, iris and wine datasets, in comparison to different versions of LDA.

Keywords: Discriminant vectors, weighted LDA, uncorrelation, principle components, Fisher-face method, Bootstarp method.

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Authors: C.Natarajan, S.K.Ayyaswamy

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Let G be a fuzzy graph. Then D Ôèå V is said to be a strong (weak) fuzzy dominating set of G if every vertex v ∈ V -D is strongly (weakly) dominated by some vertex u in D. We denote a strong (weak) fuzzy dominating set by sfd-set (wfd-set). The minimum scalar cardinality of a sfd-set (wfd-set) is called the strong (weak) fuzzy domination number of G and it is denoted by γsf (G)γwf (G). In this paper we introduce the concept of strong (weak) domination in fuzzy graphs and obtain some interesting results for this new parameter in fuzzy graphs.

Keywords: Fuzzy graphs, fuzzy domination, strong (weak) fuzzy domination number.

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Authors: N. Kaewpraek, W. Assawinchaichote

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This paper considers an H_∞ TS fuzzy state-derivative feedback controller for a class of nonlinear dynamical systems. A Takagi-Sugeno (TS) fuzzy model is used to approximate a class of nonlinear dynamical systems. Then, based on a linear matrix inequality (LMI) approach, we design an H_∞ TS fuzzy state-derivative feedback control law which guarantees L₂-gain of the mapping from the exogenous input noise to the regulated output to be less or equal to a prescribed value. We derive a sufficient condition such that the system with the fuzzy controller is asymptotically stable and H_∞ performance is satisfied. Finally, we provide and simulate a numerical example is provided to illustrate the stability and the effectiveness of the proposed controller.

Keywords: H∞ fuzzy control, LMI, Takagi-Sugano (TS) fuzzy model, nonlinear dynamic systems, state-derivative feedback.

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Authors: E. Koyuncu

Abstract:

The problem of Order Acceptance and Scheduling (OAS) is defined as a joint decision of which orders to accept for processing and how to schedule them. Any linear programming model representing real-world situation involves the parameters defined by the decision maker in an uncertain way or by means of language statement. Fuzzy data can be used to incorporate vagueness in the real-life situation. In this study, a fuzzy mathematical model is proposed for a single machine OAS problem, where the orders are defined by their fuzzy due dates, fuzzy processing times, and fuzzy sequence dependent setup times. The signed distance method, one of the fuzzy ranking methods, is used to handle the fuzzy constraints in the model.

Keywords: Fuzzy mathematical programming, fuzzy ranking, order acceptance, single machine scheduling.

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Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil

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Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.

Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Jianfeng Li, Feng Jin, Xiaoyu Yang

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This paper applies fuzzy clustering algorithm in classifying real estate companies in China according to some general financial indexes, such as income per share, share accumulation fund, net profit margins, weighted net assets yield and shareholders' equity. By constructing and normalizing initial partition matrix, getting fuzzy similar matrix with Minkowski metric and gaining the transitive closure, the dynamic fuzzy clustering analysis for real estate companies is shown clearly that different clustered result change gradually with the threshold reducing, and then, it-s shown there is the similar relationship with the prices of those companies in stock market. In this way, it-s great valuable in contrasting the real estate companies- financial condition in order to grasp some good chances of investment, and so on.

Keywords: Fuzzy clustering algorithm, data mining, real estate company, financial analysis.

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Authors: Sudipta Majumdar

Abstract:

This paper presents a method for identification of a linear time invariant (LTI) autonomous all pole system using singular value decomposition. The novelty of this paper is two fold: First, MUSIC algorithm for estimating complex frequencies from real measurements is proposed. Secondly, using the proposed algorithm, we can identify the coefficients of differential equation that determines the LTI system by switching off our input signal. For this purpose, we need only to switch off the input, apply our complex MUSIC algorithm and determine the coefficients as symmetric polynomials in the complex frequencies. This method can be applied to unstable system and has higher resolution as compared to time series solution when, noisy data are used. The classical performance bound, Cramer Rao bound (CRB), has been used as a basis for performance comparison of the proposed method for multiple poles estimation in noisy exponential signal.

Keywords: MUSIC algorithm, Cramer Rao bound, frequency estimation.

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Authors: I. Nižetić Kosović, T. Jagušt

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Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved.

Keywords: Indoor environment, received signal strength indicator, weighted centroid localization, wireless localization.

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Authors: Mustafa Bayram Gücen, Coşkun Yakar

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In this study, we have investigated the strict stability of fuzzy differential systems and we compare the classical notion of strict stability criteria of ordinary differential equations and the notion of strict stability of fuzzy differential systems. In addition that, we present definitions of stability and strict stability of fuzzy differential equations and also we have some theorems and comparison results. Strict Stability is a different stability definition and this stability type can give us an information about the rate of decay of the solutions. Lyapunov’s second method is a standard technique used in the study of the qualitative behavior of fuzzy differential systems along with a comparison result that allows the prediction of behavior of a fuzzy differential system when the behavior of the null solution of a fuzzy comparison system is known. This method is a usefull for investigating strict stability of fuzzy systems. First of all, we present definitions and necessary background material. Secondly, we discuss and compare the differences between the classical notion of stability and the recent notion of strict stability. And then, we have a comparison result in which the stability properties of the null solution of the comparison system imply the corresponding stability properties of the fuzzy differential system. Consequently, we give the strict stability results and a comparison theorem. We have used Lyapunov second method and we have proved a comparison result with scalar differential equations.

Keywords: Fuzzy systems, fuzzy differential equations, fuzzy stability, strict stability.

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Authors: Paratibha Aggarwal, Yogesh Aggarwal

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The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28- day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy logic model showed better performance than neural network model.

Keywords: Self compacting concrete, compressive strength, prediction, neural network, Fuzzy logic.

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Authors: S. A. Taher, A. Shemshadi

Abstract:

Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbance. Traditional PSS rely on robust linear design method in an attempt to cover a wider range of operating condition. Expert or rule-based controllers have also been proposed. Recently fuzzy logic (FL) as a novel robust control design method has shown promising results. The emphasis in fuzzy control design center is around uncertainties in the system parameters & operating conditions. In this paper a novel Robust Fuzzy Logic Power System Stabilizer (RFLPSS) design is proposed The RFLPSS basically utilizes only one measurable Δω signal as input (generator shaft speed). The speed signal is discretized resulting in three inputs to the RFLPSS. There are six rules for the fuzzification and two rules for defuzzification. To provide robustness, additional signal namely, speed are used as inputs to RFLPSS enabling appropriate gain adjustments for the three RFLPSS inputs. Simulation studies show the superior performance of the RFLPSS compared with an optimally designed conventional PSS and discrete mode FLPSS.

Keywords: Controller design, Fuzzy Logic, PID, Power SystemStabilizer, Robust control.

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Authors: Payam Nabhani, Sima Radmanesh

Abstract:

Opportunistic network is a kind of Delay Tolerant Networks (DTN) where the nodes in this network come into contact with each other opportunistically and communicate wirelessly and, an end-to-end path between source and destination may have never existed, and disconnection and reconnection is common in the network. In such a network, because of the nature of opportunistic network, perhaps there is no a complete path from source to destination for most of the time and even if there is a path; the path can be very unstable and may change or break quickly. Therefore, routing is one of the main challenges in this environment and, in order to make communication possible in an opportunistic network, the intermediate nodes have to play important role in the opportunistic routing protocols. In this paper we proposed an Adaptive Fuzzy Routing in opportunistic network (AFRON). This protocol is using the simple parameters as input parameters to find the path to the destination node. Using Message Transmission Count, Message Size and Time To Live parameters as input fuzzy to increase delivery ratio and decrease the buffer consumption in the all nodes of network.

Keywords: Opportunistic Routing, Fuzzy Routing, Opportunistic Network, Message Routing.

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