Search results for: Ordered fuzzy point
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2676

Search results for: Ordered fuzzy point

2526 Application of 0-1 Fuzzy Programming in Optimum Project Selection

Authors: S. Sadi-Nezhad, K. Khalili Damghani, N. Pilevari

Abstract:

In this article, a mathematical programming model for choosing an optimum portfolio of investments is developed. The investments are considered as investment projects. The uncertainties of the real world are associated through fuzzy concepts for coefficients of the proposed model (i. e. initial investment costs, profits, resource requirement, and total available budget). Model has been coded by using LINGO 11.0 solver. The results of a full analysis of optimistic and pessimistic derivative models are promising for selecting an optimum portfolio of projects in presence of uncertainty.

Keywords: Fuzzy Programming, Fuzzy Knapsack, FuzzyCapital Budgeting, Fuzzy Project Selection

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2525 Change Detector Combination in Remotely Sensed Images Using Fuzzy Integral

Authors: H. Nemmour, Y. Chibani

Abstract:

Decision fusion is one of hot research topics in classification area, which aims to achieve the best possible performance for the task at hand. In this paper, we investigate the usefulness of this concept to improve change detection accuracy in remote sensing. Thereby, outputs of two fuzzy change detectors based respectively on simultaneous and comparative analysis of multitemporal data are fused by using fuzzy integral operators. This method fuses the objective evidences produced by the change detectors with respect to fuzzy measures that express the difference of performance between them. The proposed fusion framework is evaluated in comparison with some ordinary fuzzy aggregation operators. Experiments carried out on two SPOT images showed that the fuzzy integral was the best performing. It improves the change detection accuracy while attempting to equalize the accuracy rate in both change and no change classes.

Keywords: change detection, decision fusion, fuzzy logic, remote sensing.

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2524 A Combined Fuzzy Decision Making Approach to Supply Chain Risk Assessment

Authors: P. Moeinzadeh, A. Hajfathaliha

Abstract:

Many firms implemented various initiatives such as outsourced manufacturing which could make a supply chain (SC) more vulnerable to various types of disruptions. So managing risk has become a critical component of SC management. Different types of SC vulnerability management methodologies have been proposed for managing SC risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce SC risk management by proposing an integrated approach. SC risks are identified and a risk index classification structure is created. Then we develop a SC risk assessment approach based on the analytic network process (ANP) and the VIKOR methods under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. By using FANP, risks weights are calculated and then inserted to the FVIKOR to rank the SC members and find the most risky partner.

Keywords: Analytic network process (ANP), Fuzzy sets, Supply chain risk management (SCRM), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR)

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2523 A Comparative Study of P-I, I-P, Fuzzy and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive

Authors: S.R. Khuntia, K.B. Mohanty, S. Panda, C. Ardil

Abstract:

This paper presents a comparative study of various controllers for the speed control of DC motor. The most commonly used controller for the speed control of dc motor is Proportional- Integral (P-I) controller. However, the P-I controller has some disadvantages such as: the high starting overshoot, sensitivity to controller gains and sluggish response due to sudden disturbance. So, the relatively new Integral-Proportional (I-P) controller is proposed to overcome the disadvantages of the P-I controller. Further, two Fuzzy logic based controllers namely; Fuzzy control and Neuro-fuzzy control are proposed and the performance these controllers are compared with both P-I and I-P controllers. Simulation results are presented and analyzed for all the controllers. It is observed that fuzzy logic based controllers give better responses than the traditional P-I as well as I-P controller for the speed control of dc motor drives.

Keywords: Proportional-Integral (P-I) controller, Integral- Proportional (I-P) controller, Fuzzy logic control, Neuro-fuzzy control, Speed control, DC Motor drive.

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2522 A Novel Fuzzy Technique for Image Noise Reduction

Authors: Hamed Vahdat Nejad, Hameed Reza Pourreza, Hasan Ebrahimi

Abstract:

A new fuzzy filter is presented for noise reduction of images corrupted with additive noise. The filter consists of two stages. In the first stage, all the pixels of image are processed for determining noisy pixels. For this, a fuzzy rule based system associates a degree to each pixel. The degree of a pixel is a real number in the range [0,1], which denotes a probability that the pixel is not considered as a noisy pixel. In the second stage, another fuzzy rule based system is employed. It uses the output of the previous fuzzy system to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Experimental results are obtained to show the feasibility of the proposed filter. These results are also compared to other filters by numerical measure and visual inspection.

Keywords: Additive noise, Fuzzy logic, Image processing, Noise reduction.

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2521 Black Box Model and Evolutionary Fuzzy Control Methods of Coupled-Tank System

Authors: S. Yaman, S. Rostami

Abstract:

In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.

Keywords: Function tuner method, fuzzy modeling, fuzzy PID controller, genetic algorithm.

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2520 Discovery of Production Rules with Fuzzy Hierarchy

Authors: Fadl M. Ba-Alwi, Kamal K. Bharadwaj

Abstract:

In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases.A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute values and weak classes during the fuzzy hierarchy generation.Experimental results have established the effectiveness of the proposed algorithm.

Keywords: Data Mining, Degree of subsumption, Freq matrix, Fuzzy hierarchy.

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2519 Some Properties of Superfuzzy Subset of a Fuzzy Subset

Authors: Hassan Naraghi

Abstract:

In this paper, we define permutable and mutually permutable fuzzy subgroups of a group. Then we study their relation with permutable and mutually permutable subgroups of a group. Also we study some properties of fuzzy quasinormal subgroup. We define superfuzzy subset of a fuzzy subset and we study some properties of superfuzzy subset of a fuzzy subset.

Keywords: Permutable fuzzy subgroup, mutually permutable fuzzy subgroup, fuzzy quasinormal subgroup, superfuzzy subset.

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2518 Feedrate Optimization for Ball-end milling of Sculptured Surfaces using Fuzzy Logic Controller

Authors: Njiri J. G., Ikua B. W., Nyakoe G. N.

Abstract:

Optimization of cutting parameters important in precision machining in regards to efficiency and surface integrity of the machined part. Usually productivity and precision in machining is limited by the forces emanating from the cutting process. Due to the inherent varying nature of the workpiece in terms of geometry and material composition, the peak cutting forces vary from point to point during machining process. In order to increase productivity without compromising on machining accuracy, it is important to control these cutting forces. In this paper a fuzzy logic control algorithm is developed that can be applied in the control of peak cutting forces in milling of spherical surfaces using ball end mills. The controller can adaptively vary the feedrate to maintain allowable cutting force on the tool. This control algorithm is implemented in a computer numerical control (CNC) machine. It has been demonstrated that the controller can provide stable machining and improve the performance of the CNC milling process by varying feedrate.

Keywords: Ball-end mill, feedrate, fuzzy logic controller, machining optimization, spherical surface.

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2517 From Type-I to Type-II Fuzzy System Modeling for Diagnosis of Hepatitis

Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

Abstract:

Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: Hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection.

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2516 A New Approach of Fuzzy Methods for Evaluating of Hydrological Data

Authors: Nasser Shamskia, Seyyed Habib Rahmati, Hassan Haleh , Seyyedeh Hoda Rahmati

Abstract:

The main criteria of designing in the most hydraulic constructions essentially are based on runoff or discharge of water. Two of those important criteria are runoff and return period. Mostly, these measures are calculated or estimated by stochastic data. Another feature in hydrological data is their impreciseness. Therefore, in order to deal with uncertainty and impreciseness, based on Buckley-s estimation method, a new fuzzy method of evaluating hydrological measures are developed. The method introduces triangular shape fuzzy numbers for different measures in which both of the uncertainty and impreciseness concepts are considered. Besides, since another important consideration in most of the hydrological studies is comparison of a measure during different months or years, a new fuzzy method which is consistent with special form of proposed fuzzy numbers, is also developed. Finally, to illustrate the methods more explicitly, the two algorithms are tested on one simple example and a real case study.

Keywords: Fuzzy Discharge, Fuzzy estimation, Fuzzy ranking method, Hydrological data

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2515 Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data

Authors: George E. Tsekouras, Dimitris Papageorgiou, Sotiris Kotsiantis, Christos Kalloniatis, Panagiotis Pintelas

Abstract:

We develop a three-step fuzzy logic-based algorithm for clustering categorical attributes, and we apply it to analyze cultural data. In the first step the algorithm employs an entropy-based clustering scheme, which initializes the cluster centers. In the second step we apply the fuzzy c-modes algorithm to obtain a fuzzy partition of the data set, and the third step introduces a novel cluster validity index, which decides the final number of clusters.

Keywords: Categorical data, cultural data, fuzzy logic clustering, fuzzy c-modes, cluster validity index.

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2514 Neuro Fuzzy and Self Tunging Fuzzy Controller to Improve Pitch and Yaw Control Systems Resposes of Twin Rotor MIMO System

Authors: Thair Sh. Mahmoud, Tang Sai Hong, Mohammed H. Marhaban

Abstract:

In this paper, Neuro-Fuzzy based Fuzzy Subtractive Clustering Method (FSCM) and Self Tuning Fuzzy PD-like Controller (STFPDC) were used to solve non-linearity and trajectory problems of pitch AND yaw angles of Twin Rotor MIMO system (TRMS). The control objective is to make the beams of TRMS reach a desired position quickly and accurately. The proposed method could achieve control objectives with simpler controller. To simplify the complexity of STFPDC, ANFIS based FSCM was used to simplify the controller and improve the response. The proposed controllers could achieve satisfactory objectives under different input signals. Simulation results under MATLAB/Simulink® proved the improvement of response and superiority of simplified STFPDC on Fuzzy Logic Controller (FLC).

Keywords: Fuzzy Subtractive Clustering Method, Neuro Fuzzy, Self Tuning Fuzzy Controller, and Twin Rotor MIMO System.

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2513 MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification

Authors: A. Chaouachi, R. M. Kamel, K. Nagasaka

Abstract:

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.

Keywords: MPPT, neuro-fuzzy, RBFN, grid-connected, photovoltaic.

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2512 Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning

Authors: Tahseen Ahmed Jilani, Syed Muhammad Aqil Burney, C. Ardil

Abstract:

In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on frequency density based partitioning of the historical enrollment data. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting enrollments than the existing methods.

Keywords: Fuzzy logical groups, fuzzified enrollments, fuzzysets, fuzzy time series.

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2511 Meteorological Risk Assessment for Ships with Fuzzy Logic Designer

Authors: Ismail Karaca, Ridvan Saracoglu, Omer Soner

Abstract:

Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.

Keywords: Calculation of risk factor, fuzzy logic, fuzzy programming for ship, safe navigation of ships.

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2510 Deduction of Fuzzy Autocatalytic Set to Omega Algebra and Transformation Semigroup

Authors: Liew Siaw Yee, Tahir Ahmad

Abstract:

In this paper, the Fuzzy Autocatalytic Set (FACS) is composed into Omega Algebra by embedding the membership value of fuzzy edge connectivity using the property of transitive affinity. Then, the Omega Algebra of FACS is a transformation semigroup which is a special class of semigroup is shown.

Keywords: Fuzzy autocatalytic set, omega algebra, semigroup, transformation semigroup.

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2509 Speed Control of a Permanent Magnet Synchronous Machine (PMSM) Fed by an Inverter Voltage Fuzzy Control Approach

Authors: Jamel Khedri, Mohamed Chaabane, Mansour Souissi, Driss Mehdi

Abstract:

This paper deals with the synthesis of fuzzy controller applied to a permanent magnet synchronous machine (PMSM) with a guaranteed H∞ performance. To design this fuzzy controller, nonlinear model of the PMSM is approximated by Takagi-Sugeno fuzzy model (T-S fuzzy model), then the so-called parallel distributed compensation (PDC) is employed. Next, we derive the property of the H∞ norm. The latter is cast in terms of linear matrix inequalities (LMI-s) while minimizing the H∞ norm of the transfer function between the disturbance and the error ( ) ev T . The experimental and simulations results were conducted on a permanent magnet synchronous machine to illustrate the effects of the fuzzy modelling and the controller design via the PDC.

Keywords: Feedback controller, Takagi-Sugeno fuzzy model, Linear Matrix Inequality (LMI), PMSM, H∞ performance.

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2508 Fuzzy Voting in Internal Elections of Educational and Party Organizations

Authors: R. Hosseingholizadeh

Abstract:

This article presents a method for elections between the members of a group that is founded by fuzzy logic. Linguistic variables are objects for decision on election cards and deduction is based on t-norms and s-norms. In this election-s method election cards are questionnaire. The questionnaires are comprised of some questions with some choices. The choices are words from natural language. Presented method is accompanied by center of gravity (COG) defuzzification added up to a computer program by MATLAB. Finally the method is illustrated by solving two examples; choose a head for a research group-s members and a representative for students.

Keywords: fuzzy election, fuzzy electoral card, fuzzy inference, questionnaire.

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2507 H∞ Takagi-Sugeno Fuzzy State-Derivative Feedback Control Design for Nonlinear Dynamic Systems

Authors: N. Kaewpraek, W. Assawinchaichote

Abstract:

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 HTS 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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2506 Support Vector Fuzzy Based Neural Networks For Exchange Rate Modeling

Authors: Prof. Chokri SLIM

Abstract:

A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.

Keywords: Neural network, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression.

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2505 Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents

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

Abstract:

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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2504 Project Selection by Using Fuzzy AHP and TOPSIS Technique

Authors: S. Mahmoodzadeh, J. Shahrabi, M. Pariazar, M. S. Zaeri

Abstract:

In this article, by using fuzzy AHP and TOPSIS technique we propose a new method for project selection problem. After reviewing four common methods of comparing alternatives investment (net present value, rate of return, benefit cost analysis and payback period) we use them as criteria in AHP tree. In this methodology by utilizing improved Analytical Hierarchy Process by Fuzzy set theory, first we try to calculate weight of each criterion. Then by implementing TOPSIS algorithm, assessment of projects has been done. Obtained results have been tested in a numerical example.

Keywords: Fuzzy AHP, Project Selection, TOPSIS Technique.

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2503 Fuzzy Power Controller Design for Purdue University Research Reactor-1

Authors: Oktavian Muhammad Rizki, Appiah Rita, Lastres Oscar, Miller True, Chapman Alec, Tsoukalas Lefteri H.

Abstract:

The Purdue University Research Reactor-1 (PUR-1) is a 10 kWth pool-type research reactor located at Purdue University’s West Lafayette campus. The reactor was recently upgraded to use entirely digital instrumentation and control systems. However, currently, there is no automated control system to regulate the power in the reactor. We propose a fuzzy logic controller as a form of digital twin to complement the existing digital instrumentation system to monitor and stabilize power control using existing experimental data. This work assesses the feasibility of a power controller based on a Fuzzy Rule-Based System (FRBS) by modelling and simulation with a MATLAB algorithm. The controller uses power error and reactor period as inputs and generates reactivity insertion as output. The reactivity insertion is then converted to control rod height using a logistic function based on information from the recorded experimental reactor control rod data. To test the capability of the proposed fuzzy controller, a point-kinetic reactor model is utilized based on the actual PUR-1 operation conditions and a Monte Carlo N-Particle simulation result of the core to numerically compute the neutronics parameters of reactor behavior. The Point Kinetic Equation (PKE) was employed to model dynamic characteristics of the research reactor since it explains the interactions between the spatial and time varying input and output variables efficiently. The controller is demonstrated computationally using various cases: startup, power maneuver, and shutdown. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the reactor power to follow demand power without compromising nuclear safety measures.

Keywords: Fuzzy logic controller, power controller, reactivity, research reactor.

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2502 Robust Steam Temperature Regulation for Distillation of Essential Oil Extraction Process using Hybrid Fuzzy-PD plus PID Controller

Authors: Nurhani Kasuan, Zakariah Yusuf, Mohd Nasir Taib, Mohd Hezri Fazalul Rahiman, Nazurah Tajuddin, Mohd Azri Abdul Aziz

Abstract:

This paper presents a hybrid fuzzy-PD plus PID (HFPP) controller and its application to steam distillation process for essential oil extraction system. Steam temperature is one of the most significant parameters that can influence the composition of essential oil yield. Due to parameter variations and changes in operation conditions during distillation, a robust steam temperature controller becomes nontrivial to avoid the degradation of essential oil quality. Initially, the PRBS input is triggered to the system and output of steam temperature is modeled using ARX model structure. The parameter estimation and tuning method is adopted by simulation using HFPP controller scheme. The effectiveness and robustness of proposed controller technique is validated by real time implementation to the system. The performance of HFPP using 25 and 49 fuzzy rules is compared. The experimental result demonstrates the proposed HFPP using 49 fuzzy rules achieves a better, consistent and robust controller compared to PID when considering the test on tracking the set point and the effects due to disturbance.

Keywords: Fuzzy Logic controller, steam temperature, steam distillation, real time control.

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2501 An Application of Generalized Fuzzy Soft Sets in a Social Decision Making Problem

Authors: Nisha Singhal, Usha Chouhan

Abstract:

At present, application of the extension of soft set theory in decision making problems in day to day life is progressing rapidly. The concepts of fuzzy soft set and its properties have been evolved as an area of interest for the researchers. The generalization of the concepts recently got importance and a rapid growth in the research in this area witnessed its vital-ness. In this paper, an application of the concept of generalized fuzzy soft set to make decision in a social problem is presented. Further, this paper also highlights some of the key issues of the related areas.

Keywords: Soft set, Fuzzy Soft set, Generalized Fuzzy Soft set, Membership and Non-Membership Score.

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2500 Simplex Method for Solving Linear Programming Problems with Fuzzy Numbers

Authors: S. H. Nasseri, E. Ardil, A. Yazdani, R. Zaefarian

Abstract:

The fuzzy set theory has been applied in many fields, such as operations research, control theory, and management sciences, etc. In particular, an application of this theory in decision making problems is linear programming problems with fuzzy numbers. In this study, we present a new method for solving fuzzy number linear programming problems, by use of linear ranking function. In fact, our method is similar to simplex method that was used for solving linear programming problems in crisp environment before.

Keywords: Fuzzy number linear programming, rankingfunction, simplex method.

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2499 Half-Circle Fuzzy Number Threshold Determination via Swarm Intelligence Method

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

Abstract:

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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2498 Fuzzy Control of Macroeconomic Models

Authors: Andre A. Keller

Abstract:

The optimal control is one of the possible controllers for a dynamic system, having a linear quadratic regulator and using the Pontryagin-s principle or the dynamic programming method . Stochastic disturbances may affect the coefficients (multiplicative disturbances) or the equations (additive disturbances), provided that the shocks are not too great . Nevertheless, this approach encounters difficulties when uncertainties are very important or when the probability calculus is of no help with very imprecise data. The fuzzy logic contributes to a pragmatic solution of such a problem since it operates on fuzzy numbers. A fuzzy controller acts as an artificial decision maker that operates in a closed-loop system in real time. This contribution seeks to explore the tracking problem and control of dynamic macroeconomic models using a fuzzy learning algorithm. A two inputs - single output (TISO) fuzzy model is applied to the linear fluctuation model of Phillips and to the nonlinear growth model of Goodwin.

Keywords: fuzzy control, macroeconomic model, multiplier - accelerator, nonlinear accelerator, stabilization policy.

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2497 Fuzzy Logic PID Control of Automatic Voltage Regulator System

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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