Showing papers on "Fuzzy logic published in 1999"

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

Abstract: This paper describes an experiment on the “linguistic” synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Soft set theory—First results

Abstract: The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future.

Fuzzy hierarchical analysis

Abstract: We present a new method of finding the fuzzy weights in fuzzy hierarchical analysis which is the direct fuzzification of the original method used by Saaty (1980) in the analytic hierarchy process. We test our new procedure in two cases where there are formulas for the crisp weights. An example is presented where there are five criteria and three alternatives.

Type-2 fuzzy logic systems

Abstract: We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Abstract: Pattern Recognition.- Cluster Analysis for Object Data.- Cluster Analysis for Relational Data.- Classifier Design.- Image Processing and Computer Vision.

Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition

Abstract: Introduction. Basic Concepts. Classical Fuzzy Clustering Algorithms. Linear and Ellipsoidal Prototypes Shell Prototypes. Polygonal Object Boundaries. Cluster Estimation Models. Cluster Validity. Rule Generation with Clustering. Appendix. Bibliography.

A Unifying Field in Logics: Neutrosophic Logic.

Abstract: The author makes an introduction to non-standard analysis, then extends the dialectics to “neutrosophy” – which became a new branch of philosophy. This new concept helps in generalizing the intuitionistic, paraconsistent, dialetheism, fuzzy logic to “neutrosophic logic” – which is the first logic that comprises paradoxes and distinguishes between relative and absolute truth. Similarly, the fuzzy set is generalized to “neutrosophic set”. Also, the classical and imprecise probabilities are generalized to “neutrosophic probability”.

Mathematical Principles of Fuzzy Logic

Abstract: Preface. 1. Fuzzy Logic: What, Why, for Which? 2. Algebraic Structures for Logical Calculi. 3. Logical Calculi and Model Theory. 4. Fuzzy Logic in Narrow Sense. 5. Functional Systems in Fuzzy Logic Theories. 6. Fuzzy Logic in Broader Sense. 7. Topoi and Categories of Fuzzy Sets. 8. Few Historical and Concluding Remarks. References. Index.

Similarity measures

Abstract: With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a definition of similarity as an operation. We develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. The model is dubbed fuzzy feature contrast (FFC) and is an extension to a more general domain of the feature contrast model due to Tversky (1977). We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties.

A robust self-tuning scheme for PI- and PD-type fuzzy controllers

Abstract: Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error (e) and change of error (/spl Delta/e) of the controlled variable using the most natural and unbiased membership functions (MFs). The proposed self-tuning technique is applied to both PI- and PD-type FLCs to conduct simulation analysis for a wide range of different linear and nonlinear second-order processes including a marginally stable system where even the well known Ziegler-Nichols tuned conventional PI or PID controllers fail to provide an acceptable performance due to excessively large overshoot. Performances of the proposed self-tuning FLCs are compared with those of their corresponding conventional FLCs in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error and integral-of-time-multiplied absolute error, in addition to the responses due to step set-point change and load disturbance and, in each case, the proposed scheme shows a remarkably improved performance over its conventional counterpart.

Stabilizing controller design for uncertain nonlinear systems using fuzzy models

Abstract: A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

A fuzzy k-modes algorithm for clustering categorical data

Abstract: This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.

A survey of fuzzy clustering algorithms for pattern recognition. II

Abstract: For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms.

Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems

Abstract: We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule Thus, our method can be viewed as a classifier system In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained The fixed membership functions also lead to a simple implementation of our method as a computer program The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method The performance of our method is evaluated by computer simulations on some well-known test problems While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks

Cloud Microphysics Retrieval Using S-Band Dual-Polarization Radar Measurements

Abstract: Recent studies have shown the utility of polarimetric radar observables and derived fields for discrimination of hydrometeor particle types. Because the values of the radar observables that delineate different particle types overlap and are not sharply defined, the problem is well suited for a fuzzy logic approach. In this preliminary study the authors have developed and implemented a fuzzy logic algorithm for hydrometeor particle identification that is simple and efficient enough to run in real time for operational use. Although there are no in situ measurements available for this particle-type verification, the initial results are encouraging. Plans for further verification and optimization of the algorithm are described.

Robustness design of nonlinear dynamic systems via fuzzy linear control

Abstract: This study introduces a fuzzy linear control design method for nonlinear systems with optimal H/sup /spl infin// robustness performance. First, the Takagi and Sugeno fuzzy linear model (1985) is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, H/sup /spl infin// performance design can be achieved in nonlinear control systems. In the proposed fuzzy linear control method, the fuzzy linear model provides rough control to approximate the nonlinear control system, while the H/sup /spl infin// scheme provides precise control to achieve the optimal robustness performance. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. In the case that state variables are unavailable, a fuzzy observer-based H/sup /spl infin// control is also proposed to achieve a robust optimization design for nonlinear systems. A simulation example is given to illustrate the performance of the proposed design method.

Mathematics Behind Fuzzy Logic

Abstract: Residuated Lattices: Lattices and Equivalence Relations Lattice Filters Residuated Lattices BL-Algebras Exercises.- MV-Algebras: MV-Algebras and Wajsberg Algebras Complete MV-Algebras Pseudo-Boolean Algebras Exercises.- Fuzzy Propositional Logic: Semantics of Fuzzy Propositional Logic Exercises.- Fuzzy Relations: Solvability of Fuzzy Relation Equations On Fuzzy Similiarity Relations Fuzzy Similiarity and Approximate Reasoning Maximal Similiarity and Fuzzy Reasoning Exercises.- Solutions to Exercises.

Case-based reasoning is a methodology not a technology

Abstract: This paper asks whether case-based reasoning is an artificial intelligence (AI) technology like rule-based reasoning, neural networks or genetic algorithms or whether it is better described as a methodology for problem solving, that may use any appropriate technology. By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. The implications of this are discussed.

Adaptive fuzzy gain scheduling for load frequency control

Abstract: An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers has been proposed. A Sugeno type fuzzy inference system is used in the proposed controller. The Sugeno type fuzzy inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a very nonlinear system moves around in its operating space. The proposed adaptive controller requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions.

Computing with Words in Information/Intelligent Systems 1

Abstract: M Sugeno: Foreword- Neuro-Fuzzy and Genetic Systems for Computing with Words: S Mitaim, B Kosko: Neural Fuzzy Intelligent Agents S Siekmann, R Neuneier, H-G Zimmermann, R Kruse: Neuro Fuzzy Systems for Data Analysis J Leski, E Czogala: A New Fuzzy Interference System Based on Artificial Neural Network and its Applications O Cordon, A Gonzales, F Herrera, R Perez: Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling- Tools for Linguistic Data Modeling and Analysis: H Lee, H Tanaka: Fuzzy Graphs with Linguistic Input-Outputs by Fuzzy Approximation Models MA Gil, PA Gil, DA Ralescu: Fuzzy Random Variables: Modeling Linguistic Statistical Data- Linguistic Models in System Reliability, Quality Control and Risk Analysis: T Onisawa, A Ohmori: Linguistic Model of System Reliability Analysis P Grzegorzewski, O Hryniewicz: Lifetime Tests for Vague Data C Huang, D Ruan: Systems Analytic Models for Fuzzy Risk Estimation- Linguistic Models in Decision Making, Optimization and Control: H Kiendl: Decision Analysis by Advanced Fuzzy Systems J kacprzyk, H Nurmi, M Fedrizzi: Group Decision Making and a Measure of Consensus under Fuzzy Preferences and a Fuzzy Linguistic Majority S Chanas, D Kuchta: Linear Programming with Words JJ Buckley, T Feuring: Computing with Words in Control R Kowalczyk: On Linguistic Fuzzy Constraint Satisfaction Problems- Linguistic and Imprecise Information in Databases and Information Systems: G Chen: Data Models for Dealing with Linguistic and Imprecise Information FE Petry, M Cobb, A Morris: Fuzzy Set Approaches to Model Uncertainty in Spatial Data and Geographic Information Systems JC Cubero, JM Medina, OPons, MA Vila: Computing Fuzzy Dependencies with Linguistic Labels J Kacprzyk, S Zadrozny: The Paradigm of Computing with Words in Intelligent Database Querying W Pedrycz: Lingusitic Data Mining RA Bustos, TD Gedeon: Evaluation of Connectionist Information Retrieval in a Legal Document Collection- Applications Information in Databases and Information Systems: ME Cohen, DL Hudson: Using Linguistic Models in Medical Decision Making JM Mendel, S Murphy, LC Miller, M Martin, N Karnik: The Fuzzy Logic Advisor for Social Judgements: A First Attempt J Zelger, AG de Wet, A-M Pothas, D Petkov: Conceptualisation with GABEK: Ideas on Social Change in South Africa F Herrera, E Lopez, C Manadana, M Rodriguez: A Linguistic Decision Model to Suppliers Selection in International Purchasing L Zerrouki, B Bouchon-Meunier, R Fondacci: Fuzzy System for Air Traffic Flow Management G Michalik, W Mielczarski: A Fuzzy Approach to Contracting Electrical Energy in Competitive Electricity Markets D Ruan: Fuzzy Logic and Intelligent Computing in Nuclear Engineering A Filippidis, LC Jain, NM Martin: Computational Intelligence Techniques in Landmine Detection

Analysis of direct action fuzzy PID controller structures

Abstract: The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.