Showing papers on "Neuro-fuzzy published in 1987"

Fuzzy sets and applications : selected papers

Abstract: Coping with the Imprecision of the Real World: An Interview with L.A Zadeh Fuzzy Sets Probability Measures of Fuzzy Events Decision Making in a Fuzzy Environment Similarity Relations and Fuzzy Orderings Outline of a New Approach to the Analysis of Complex Systems and Decision Processes A Fuzzy-Algorithmic Approach to the Definition of Complex or Imprecise Comcepts Fuzzy Sets as a Basis for a Theory of Possibility The Concept of a Linguistic Variable and Its Application to Approximate Reasoning (Part 1) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning (Part 2) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning (Part 3) A Theory of Approximate Reasoning The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems Syllogistic Reasoning in Fuzzy Logic and its Application to Usuality and Reasoning with Dispositions A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges PRUF-A Meaning Representation Language for Natural Languages A Computational Approach to Fuzzy Quantifiers in Natural Language A Theory of Commonsense Knowledge Test-Score Semantics as a Basis for a Computational Approach to the Representation of Meaning.

Fuzzy Model Identification and Self-Learning for Dynamic Systems

Abstract: The algorithms of fuzzy model identification and self-learning for multi-input/multi-output dynamic systems are proposed. The required computer capacity and time for implementing the proposed algorithms and related resulting models are-significantly reduced by introducing the concept of the "referential fuzzy sets." Two numerical examples are given to show that the proposed algorithms can provide the fuzzy models with satisfactory accuracy.

Optical neural computers

Abstract: Reference EPFL-ARTICLE-158542doi:10.1038/scientificamerican0387-88View record in Web of Science Record created on 2010-11-25, modified on 2017-05-10

Comparing fuzzy logic with classical controller designs

Abstract: A unified approach for comparing the performance of fuzzy and nonfuzzy controller designs is presented. Relationships are established between the gain parameters for the two classes of controller designs. The methods presented apply equally well to proportional-plus-integral, linear multiband, and multilevel relay controllers.

Multi‐objective optimization of fuzzy structural systems

Abstract: It is recognized that there exists a vast amount of fuzzy information in both the objective and constraint functions of the optimum design of structures. Since most practical structural design problems involve several, often conflicting, objectives to be considered, a multi-objective fuzzy programming method is outlined in this work. The fuzzy constraints define a fuzzy feasible domain in the design space and each of the fuzzy objective functions defines the optimum solution by a fuzzy set of points. A method of solving a fuzzy multi-objective structural optimization problem using ordinary single-objective programming techniques is presented. The computational approach is illustrated with two numerical examples.

Fuzzy logic computers and circuits

Abstract: A fuzzy computer basically includes a plurality of fuzzy membership function generator circuits, and a fuzzy inference engine for executing a predetermined fuzzy operation among fuzzy membership functions that have been generated. A fuzzy membership function is represented by electric signals distributed on a plurality of lines.

A parallel rule firing fuzzy production system with resolution of memory conflicts by weak fuzzy monoticity, applied to the classification of multiple objects characterized by multiple uncertain features

Abstract: A fuzzy production system shell is described characterized by parallel rather than sequential rule firing. All fireable rules are fired in effect concurrently. Since there is no unfired-rule stack, no backtracking can take place, and no rule conflict algorithm is necessary; instead, a memory conflict algorithm is invoked when more than one rule modifies the same datum. Memory conflicts are resolved by weakly monotonie fuzzy logic; i.e. the value or truth value of an attribute may be replaced if the new truth value is equal to or greater than the old truth value. The system depends heavily on the use of fuzzy logic and on confidence levels, fuzzy numbers and fuzzy sets as explicit data types, and on the generation of rules from a data base of expert knowledge. Fuzzy sets are used to store contradictory and ambiguous information and results. If a problem is suitable for parallel processing, substantial reductions in system overhead are achieved, together with substantial economy in the number of rules which must be written; if a problem is not suitable for parallel processing, no economy is achieved. We suggest that problems which yield to deductive reasoning constitute a class which is suitable for sequential rule firing, and problems which yield to inductive reasoning constitute a class suitable for parallel processing. A successful application of the system to the unsupervised analysis of a time sequence of noisy echocardiogram images is described.

Optimum design of structures in a fuzzy environment

Abstract: The basic concepts of the theory of fuzzy sets and its applications are briefly reviewed. A method is then presented for solving fuzzy optimization problems using ordinary nonlinear programming techniques along with a numerical example. Possible applications of the approach described here are mentioned, including application to the analysis of structural failures and development of expert systems for structural design. 9 references.

Fuzzy Optimization and Mathematical Programming: A Brief Introduction and Survey

Abstract: Some general concepts and ideas related to fuzzy optimization as, e.g., a fuzzy constraint, fuzzy goal (objective function), fuzzy optimum, etc. are introduced first. A general fuzzy optimization problem involving these elements is formulated and solved. The cases of single and multiple objective functions are dealt with. Secondly, basic classes of fuzzy mathematical programming are discussed, including: fuzzy linear programming (with single and multiple objective functions), fuzzy integer programming, fuzzy 0–1 programming and fuzzy dynamic programming. Finally some newer, knowledge-based approaches are mentioned. An extended list of literature is included.

On Fuzzy Location Models

Abstract: Typically, in location problems “optimisation” is judged according to criteria which are mainly deterministic and crisply defined. In a great many location problems, however, the policy and decision makers are not always in a position to determine crisply either the criteria on the basis of which optimisation will be assumed or the aims and objectives to be achieved or even the restrictions imposed on the overall project. This paper presents some fuzzy location models and solution methodologies which can be applied to such situations. The emphasis here is on the application of fuzzy discrete location models on i) The general mixed integer programming formulation of the discrete model, commonly known as “the simple plant location model”, and ii) the set covering and set partitioning 0–1 pure integer programming formulations.

Functional requirements for a fuzzy expert system shell

Abstract: There are several general-purpose fuzzy expert system shells now in existence. However, experience with our system FLOPS indicates that several hurdles must be overcome before widespread successful use of the full power of such fuzzy reaoning systems can be confidently expected. Many of these hurdles confront non-fuzzy systems as well. Major developments needed include advances in appropriate fuzzy systems theory in several areas; abstract definitions of problem domain which transcend particular fields of application; standardization of blackboard architecture; standardized interfaces to blackboards and data bases? standardized interfaces to procedural languages; a sharp increase in production system computational efficiency; and software tools for development and debugging of rules. Theoretical developments badly needed include use of prior associations in fuzzy logic, i.e. dealing with the problems that have plagued Bayes' theorem ior over a hundred years; development of a theory of nonmonotonic fuzzy logic; further development of the theory of fuzzy numbers, especially in settheoretic operations and inequalities; narrowing the field of set-theoretic operations on fuzzy sets by tieing particular operators to their sphere of usefulness, and extending them to fuzzy sets of higher levels and types; creating theories of automated fuzzy reasoning, deductive and inductive; and refining a theory of fuzzy logic which takes account of the special role of expert system rules as replacement operators rather than logical propositions.