Showing papers on "Fuzzy associative matrix published in 1990"

Fuzzy logic in control systems: fuzzy logic controller. II

Abstract: For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from laboratory level to industrial process control, are briefly reported. Some unsolved problems are described, and further challenges in this field are discussed. >

Designing with fuzzy logic

Abstract: Fuzzy logic is examined, and its application to control systems is discussed. The steps taken to design a fuzzy controller are described, and the possibility of interfacing fuzzy logic to existing control systems is noted. Tools for developing and modeling fuzzy control systems are described. >

A Fuzzy Prolog Database System

Abstract: Until this book, the available literature on fuzzy sets has been, at best, scattered throughout industrial and university libraries. Encapsulated here is a sound discussion of the basic theoretical and practical aspects involved in fuzzy database systems. With a practical, hands-on, applications-oriented approach, it develops computer models for applications to decision-making processes, introducing the basic notion of relative grades via the fuzzy set theoretic approach. Also covers fuzzy relational databases and their calculus, and the fuzzy relational (structured) query language (FSQL). The last sections present methods for treating the incomplete information in fuzzy PROLOG database (FPDB) systems. Several examples of knowledge representation, expert systems, fuzzy control, and fuzzy clustering and information retrieval illustrate the theory. An extended sample database is used throughout the book.

A Neural Expert System with Automated Extraction of Fuzzy If-Then Rules and Its Application to Medical Diagnosis

Abstract: This paper proposes a fuzzy neural expert system (FNES) with the following two functions: (1) Generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; (2) Extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (If-part) from a trained neural network. This paper also gives a method to extract automatically fuzzy If-Then rules from the trained neural network. To prove the effectiveness and validity of the proposed fuzzy neural expert system, a fuzzy neural expert system for medical diagnosis has been developed.

Fuzzy sets in information retrieval and cluster analysis

Abstract: 1 Introduction.- 1.1 The Subject.- 1.2 Information Retrieval.- 1.3 Hierarchical Cluster Analysis.- 1.4 A Pragmatic Approach.- 1.5 Principles of Mathematical Models.- 1.6 Outline of the Contents.- 2 Fuzzy Sets.- 2.1 Crisp Sets and Fuzzy Sets.- 2.2 Set Operations.- 2.3 Basic Properties of Fuzzy Sets.- 2.4 Image of a Fuzzy Set, Convexity.- 2.5 Measures on Fuzzy Sets.- 2.6 Fuzzy Relations.- 2.7 Crisp Graphs.- 2.8 Fuzzy Graphs.- 2.9 A Max-Min Algebra and Fuzzy Graphs.- 3 Review of Information Retrieval.- 3.1 Introduction.- 3.2 A Crisp System for Information Retrieval.- 3.3 Measures for Evaluation of Information Retrieval Effectiveness.- 3.4 Associative Indices for Information Retrieval.- 3.5 The Citation Index.- 3.6 Weighted Retrieval and a Diagram for Information Retrieval.- 4 Introduction to Fuzzy Information Retrieval.- 4.1 A Simple Type of Fuzzy Information Retrieval.- 4.2 A Typology of Fuzzy Retrieval.- 5 Information Retrieval through Fuzzy Associations.- 5.1 A Mathematical Model for Fuzzy Thesauri.- 5.2 Fuzzy Associations.- 5.3 Information Retrieval Through Fuzzy Associations.- 6 Hierarchical Cluster Analysis and Fuzzy Sets.- 6.1 Introduction to Cluster Analysis.- 6.2 Measures of Similarity and Distance.- 6.3 Algorithms for Hierarchical Clustering.- 6.4 Single Linkage and Fuzzy Graph.- 6.5 Dendrogram and Tree Traversal.- 6.6 Reversal in a Dendrogram.- 6.7 A Class of New Methods.- 6.8 Application to Document Clustering.- 7 Feedback in Information Retrieval and Search for Clusters.- 7.1 Retrieval Through Clusters.- 7.2 Block Diagrams and Max-Min Algebra.- 7.3 Feedback in Block Diagrams.- 8 Other Methods in Fuzzy Information Retrieval and Related Topics.- 8.1 Other Methods in Fuzzy Information Retrieval.- 8.2 Evaluation Measures in Fuzzy Information Retrieval.- 8.3 Fuzzy Relational Database.- 8.4 A Brief Review for Studies in Fuzzy Information Retrieval.- 8.5 Development of a Fuzzy Retrieval System.- 8.6 Graphical Representations of Bibliographic Structure.- 9 Discussion and Suggestions for Further Studies.- References.

On the syntactico-semantical completeness of first-order fuzzy logic. I. Syntax and semantics

Abstract: This is the first part of the extensive paper which presents the syntax and semantics of first-order fuzzy logic. We introduce the structure of truth values and present some main properties of its. Then the language of first-order fuzzy logic and its syntax and semantics are defined, and proved many theorems demonstrating their good properties. The concept of a fuzzy theory is defined and the main properties of fuzzy theories are presented including the problem of their consistency and completeness

Fuzzy neural expert system with automated extraction of fuzzy If-Then rules from a trained neural network

Abstract: Proposes a fuzzy neural expert system (FNES) which has a feedforward fuzzy neural network whose input layer consists of fuzzy cell groups and crisp (non-fuzzy) cell groups. Here, the truthfulness of fuzzy information and crisp information of training data is represented by fuzzy cell groups and crisp cell groups, respectively. The expert system has the following two functions: generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; and extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (If-part) from a trained fuzzy neural network. The paper also gives a method to extract automatically fuzzy If-Then rules from the trained neural network. To prove the effectiveness and validity of the proposed fuzzy neural expert system, a fuzzy neural expert system for medical diagnosis has been developed. >

The Fuzzy Classifier System: Motivations and first Results

Abstract: This paper presents a proposal for a machine learning system, called the fuzzy classifier system. The fuzzy classifier system allows for inputs, outputs, and internal variables to take continuous values over given ranges. The fuzzy classifier system learns by creating fuzzy rules which relate the values of the input variables to internal or output variables. It has credit assignment and conflict resolution mechanisms which reassemble those of common classifier systems, with a fuzzy nature. The fuzzy classifier system employs a genetic algorithm to evolve adequate fuzzy rules. Preliminary results show that the fuzzy classifier system can effectively create fuzzy rules that imitate the behavior of a simple static system.

Composition methods of fuzzy neural networks

Abstract: Fuzzy neural networks (FNNs) are systems which apply neural networks to fuzzy reasoning. Two types of FNN are presented. In the first type, the consequences of fuzzy reasoning are realized by constants. In the second type, the consequences are expressed by first-order linear equations. The FNNs can automatically identify fuzzy rules and tune membership functions. Their performance on fuzzy reasoning is examined by simulations. The features of the two types of FNNs are clarified. >

Self-tuning of a fuzzy logic controller using a cell state space algorithm

Abstract: A method of automatically fine-tuning the output function parameters of a fuzzy logic controller based on the cell mapping concept is presented. The method takes a computational approach to the analysis of phase-space-based information about the global behavior of the system. The values of the output function parameters are determined with cell-state-based optimal control algorithm. The input-output data in the table are used to fine-tune the output function parameters of the fuzzy logic controller through a novel application of a mean-square-error gradient estimation algorithm. The accuracy of the cell mapping is increased by using variable time step sizes for cell transitions. The resultant fuzzy logic controller implements a smooth, highly nonlinear control surface that requires a relatively small number of rules. The validity of the method is demonstrated for the time-optimal setpoint control of a DC motor. >

A Fuzzy Neuron Based on Maximum Entropy Ordered Weighted Averaging

Abstract: There have been several interesting attempts to blend fuzzy set logic and neural networks. These include Shiue and Grondin [3], Yager [4], Taber and Deich [7], Kosko [10], Oden [8], and more recently Yamakawa and Tomoda [1] and Langheld and Goser [9]. This paper extends the work of Yamakawa and Tomoda by employing Maximum Entropy Ordered Weighted Averaging (MEOWA) operations at the soma of a fuzzy neuron. The MEOWA is an extension by O'Hagan [6, 2] of Yager's [4, 5] Ordered Weighted Averaging (OWA) operators. The advantage of using the MEOWA operator in a fuzzy neuron is that a single type IC appears to be sufficient to build a neural pattern recognition system. Neural inputs and weights are fuzzy numbers rather than the usual scalar values of "standard" neural networks. Fuzzy logic operations replace the usual summing, dot product, and nonlinear "squashing" operations in the soma. Fuzzy neurons appear to be able to perform pattern recognition with reduced resolution inputs relative to standard neural networks: O(m) + O(n) vs. O(mn).

Systems dynamics with fuzzy logic

Abstract: Systems dynamics has been used to model and simulate a variety of environments, e.g. economic, social and political, which require quantification or some types of human behaviour. The lack of empirical verification of the relationships in the systems dynamics models has often been criticised. Nevertheless, the methodology is effective in dealing with time-varying (dynamic) interactions among components of the analysed system. The effectiveness of systems dynamics as a methodology for modelling, simulating and analysing real-life systems can be significantly increased if it is extended to deal with imprecise and vague variables or events. Such an extension requires: (1) treatment of imprecise and vague input variables as fuzzy variables: (2) use of fuzzy arithmetic in the level, rate and auxiliary equations when fuzzy numbers are involved; and (3) replacement of some of the relationships in the systems dynamics models either with conditional statements including fuzzy variables, or with fuzzy algorithms.

Fuzzy control using neural network techniques

Abstract: A method of fuzzy control using a multilayer neural network which learns fuzzy rules using the error backpropagation algorithm is proposed. To demonstrate the method, a motor servo control was simulated to confirm that tracking could be conducted. The authors also investigated the relationship between fuzzy rule number or effect of learning rules and output using a three-dimensional output expression. The more the network learns, the clearer the undulation is, but the number of rules which were learned does not affect the input-output relationship seriously if rules express a similar relationship. This system was compared with an ordinary fuzzy control method presented by E.H. Mamdani (1976). In this case, the input-output relationship is rugged. It is pointed out that one of the advantages of using a neural network is insensitivity to damage. It was found that if certain important connections are severed, the effects are critical

Fuzzy Logic Inference Neural Networks

Abstract: Fuzzy Logic has gained increased attention as a methodology for managing uncertainty in a rule-based structure. In a fuzzy logic inference system, more rules can fire at any given time than in a crisp expert system and since the propositions are modelled as possibility distributions, there is a considerable computation load on the inference engine. In this paper, two neural network structures are proposed as a means of performing fuzzy logic inference. In the first structure, the knowledge of the rule (i.e., the antecedent and consequent clauses) are explicitly encoded in the weights of the net, whereas the second network in trained by example. Both theoretical properties and simulation results of these structures are included.