Showing papers on "Fuzzy logic published in 2008"

Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach

Abstract: From the Publisher: A comprehensive treatment of model-based fuzzy control systems This volume offers full coverage of the systematic framework for the stability and design of nonlinear fuzzy control systems. Building on the Takagi-Sugeno fuzzy model, authors Tanaka and Wang address a number of important issues in fuzzy control systems, including stability analysis, systematic design procedures, incorporation of performance specifications, numerical implementations, and practical applications. Issues that have not been fully treated in existing texts, such as stability analysis, systematic design, and performance analysis, are crucial to the validity and applicability of fuzzy control methodology. Fuzzy Control Systems Design and Analysis addresses these issues in the framework of parallel distributed compensation, a controller structure devised in accordance with the fuzzy model. This balanced treatment features an overview of fuzzy control, modeling, and stability analysis, as well as a section on the use of linear matrix inequalities (LMI) as an approach to fuzzy design and control. It also covers advanced topics in model-based fuzzy control systems, including modeling and control of chaotic systems. Later sections offer practical examples in the form of detailed theoretical and experimental studies of fuzzy control in robotic systems and a discussion of future directions in the field. Fuzzy Control Systems Design and Analysis offers an advanced treatment of fuzzy control that makes a useful reference for researchers and a reliable text for advanced graduate students in the field.

Computational Intelligence: An Introduction

Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Fuzzy Implications

Abstract: And how this book will influence you to do better future? It will relate to how the readers will get the lessons that are coming. As known, commonly many people will believe that reading can be an entrance to enter the new perception. The perception will influence how you step you life. Even that is difficult enough; people with high sprit may not feel bored or give up realizing that concept. It's what fuzzy implications will give the thoughts for you.

Fuzzy Process, Hybrid Process and Uncertain Process

Abstract: This paper flrst reviews difierent types of uncertainty. In order to construct fuzzy counterparts of Brownian motion and stochastic calculus, this paper proposes some basic concepts of fuzzy process, including fuzzy calculus and fuzzy difierential equation. Those new concepts are also extended to hybrid process and uncertain process. A basic stock model is presented, thus opening up a way to fuzzy flnancial mathematics.

CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks

Abstract: In designing the wireless sensor networks, the energy is the most important consideration because the lifetime of the sensor node is limited by the battery of it. To overcome this demerit many research have been done. The clustering is the one of the representative approaches. In the clustering, the cluster heads gather data from nodes, aggregate it and send the information to the base station. In this way, the sensor nodes can reduce communication overheads that may be generated if each sensor node reports sensed data to the base station independently. LEACH is one of the most famous clustering mechanisms. It elects a cluster head based on probability model. This approach may reduce the network lifetime because LEACH does not consider the distribution of sensor nodes and the energy remains of each node. However, using the location and the energy information in the clustering can generate big overheads. In this paper we introduce CHEF - cluster head election mechanism using fuzzy logic. By using fuzzy logic, collecting and calculating overheads can be reduced and finally the lifetime of the sensor networks can be prolonged. To prove efficiency of CHEF, we simulated CHEF compared with LEACH using the matlab. Our simulation results show that CHEF is about 22.7% more efficient than LEACH.

A causal analytical method for group decision-making under fuzzy environment

Abstract: Causal analysis largely influences the effectiveness of decision-making and the productivity of actions. The complex relationship between cause and effect as well as the fuzzy nature of human life make the casual analysis difficult. In this paper, we develop a fuzzy DEMATEL method for group decision-making to gather group ideas and analyze the cause-effect relationship of complex problems in fuzzy environments. Procedures of the fuzzy DEMATEL method are then proposed. Using the fuzzy DEMATEL procedures, the involved criteria of a system (or subsystem) are separated into the cause and effect groups for helping decision-makers focus on those criteria that provide great influence. An empirical study applies the proposed fuzzy DEMATEL method to the R&D project selection of a Taiwanese company. The result shows that, within the cause group, the criterion of ''probability of technical success'' is the most important factor for R&D project selection, whereas the ''strategic fit'' and ''potential size of market'' have the best effect on the other criteria. By contrast, the ''net present value'' is the most easily improved of the effect group criteria.

On new solutions of fuzzy differential equations

Abstract: We study fuzzy differential equations (FDE) using the concept of generalized H-differentiability. This concept is based in the enlargement of the class of differentiable fuzzy mappings and, for this, we consider the lateral Hukuhara derivatives. We will see that both derivatives are different and they lead us to different solutions from a FDE. Also, some illustrative examples are given and some comparisons with other methods for solving FDE are made.

Fuzzy communities and the concept of bridgeness in complex networks.

Abstract: We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We create an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that have significant membership in more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the data set analyzed. The number of communities can be given in advance, or determined by the algorithm itself, using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to, social networks, scientific collaboration networks, and cortical networks, with high confidence.

Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection

Abstract: Facility location selection is a multi-criteria decision problem and has a strategic importance for many companies. The conventional methods for facility location selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, fuzzy multi-criteria decision-making methods are proposed. The aim of this study is to use fuzzy analytic hierarchy process (AHP) and the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods for the selection of facility location. The proposed methods have been applied to a facility location selection problem of a textile company in Turkey. After determining the criteria that affect the facility location decisions, fuzzy AHP and fuzzy TOPSIS methods are applied to the problem and results are presented. The similarities and differences of two methods are also discussed.

Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems

Abstract: This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework

Abstract: High-resolution forecasts from numerical models can look quite realistic and provide the forecaster with very useful guidance. However, when verified using traditional metrics they often score quite poorly because of the difficulty of predicting an exact match to the observations at high resolution. ‘Fuzzy’ verification rewards closeness by relaxing the requirement for exact matches between forecasts and observations. The key to the fuzzy approach is the use of a spatial window or neighbourhood surrounding the forecast and/or observed points. The treatment of the data within the window may include averaging (upscaling), thresholding, or generation of a PDF, depending on the particular fuzzy method used and its implicit decision model concerning what makes a good forecast. The size of the neighbourhood can be varied to provide verification results at multiple scales, thus allowing the user to determine at which scales the forecast has useful skill. This article describes a framework for fuzzy verification that incorporates several fuzzy verification methods. It is demonstrated on a high-resolution precipitation forecast from the United Kingdom (UK) and the results interpreted to show the additional information that can be gleaned from this approach. Copyright © 2008 Royal Meteorological Society

Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments

Abstract: In trying to make a satisfactory decision when imprecise and multicriteria situations are involved, a decision maker has to use a fuzzy multicriteria decision making method. "Fuzzy Multi-Criteria Decision Making" (MCDM) presents fuzzy multiattribute and multiobjective decision-making methodologies by distinguished MCDM researchers. In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results. This book may be used as a textbook in graduate operations research, industrial engineering, and economics courses. It will also be an excellent resource, providing new suggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplines where multicriteria decision making is needed.

Evolving Fuzzy-Rule-Based Classifiers From Data Streams

Abstract: A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno ( eTS) type. The proposed approach, called eClass (evolving class ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.

Nonquadratic Stabilization Conditions for a Class of Uncertain Nonlinear Discrete Time TS Fuzzy Models: A New Approach

Abstract: The discrete-time uncertain nonlinear models are considered in a Takagi-Sugeno form and their stabilization is studied through a non- quadratic Lyapunov function. The classical conditions consider a one- sample variation, here, the main results are obtained considering k samples variation, i.e., Deltak V(x(t)) = V(x(t + k)) - V(x(t)). The results are shown to always include the classical cases, and several examples illustrate the effectiveness of the approach.

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

Abstract: This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).

Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

Abstract: Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selection described through the use of real-world applications and worked examples Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

Abstract: In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi-Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.

A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive

Abstract: This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach.