Showing papers on "Neuro-fuzzy published in 1988"

Fuzzy Sets, Uncertainty and Information

Abstract: (1990). Fuzzy Sets, Uncertainty, and Information. Journal of the Operational Research Society: Vol. 41, No. 9, pp. 884-886.

Artificial neural networks

Abstract: Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how they can be programmed and made to solve particular problems; and whether interesting problems can actually be put on such networks. The author then describes the current state of artificial neural network technology and the resulting challenges to people working on electronic devices. >

Fuzzy concepts in expert systems

Abstract: The authors present a comprehensive expert-system building tool, called System Z-II, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. This fully implemented tool has been used to build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-II is a rule-based system that uses fuzzy logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system. >

Fuzzy cognitive maps, signal flow graphs, and qualitative circuit analysis

Abstract: Fuzzy cognitive maps (FCMs) represent a means of fuzzy causal knowledge processing, using the net rather than the traditional tree knowledge representation. The FCM approach allows various knowledge bases to be combined. Similarities between the FCMs and signal flow graphs (SFGs) are pointed out and the inference process used in FCMs is compared in parallel with a fixed point iterative solution of the equations describing the SFG. Then, applications to qualitative circuit analysis are discussed for a class of feedback amplifiers and general active RLC circuits, using a combination of the SFG and FCM concepts. Several examples are given. >

Combining Fuzzy Imprecision With Probabilistic Uncertainty in Decision Making

Abstract: Essay on the history of the development of many-valued logics and some related topics.- 1. Introductory Sections.- Uncertainty aversion and separated effects in decision making under uncertainty.- Essentials of decision making under generalized uncertainty.- Decision evaluation methods under uncertainty and imprecision.- 2. Basic Theoretical Issues.- Fuzzy random variables.- Fuzzy P-measures and their application in decision making.- Theory and applications of fuzzy statistics.- Confidence intervals for the parameters of a linguistic random variable.- On combining uncertainty measures.- On the combination of vague evidence of the probabilistic origin.- Fuzzy evaluation of communicators.- Uncertain associational relations: compatibility and transition relations in reasoning.- 3. Fuzzy Sets Involving Random Aspects.- Stochastic fuzzy sets: a survey.- Probabilistic sets - a survey.- 4. Decision - Making - Related Models Involving Fuzziness and Randomness.- Decision making based on fuzzy stochastic and statistical dominance.- Decision making in a probabilistic fuzzy environment.- Randomness and fuzziness in a linear programming problem.- Comparison of methodologies for multicriteria feasibility -constrained fuzzy and multiple - objective stochastic linear programming.- Fuzzy dynamic programming with stochastic systems.- Probabilistic - possibilistic approach to some statistical problems with fuzzy experimental observations.- Estimation of life-time with fuzzy prior information: application in reliability.- Questionnaires with fuzzy and probabilistic elements.- From fuzzy data to a single action - a simulation approach.- 5. Applications.- Probabilistic sets in classification and pattern recognition.- Fuzzy optimization of radiation protection and nuclear safety.- Application of fuzzy statistical decision making in countermeasures against great earthquakes.- From an oriental market to the European monetary system: some fuzzy - sers - related ideas.

Project network analysis with fuzzy activity times

Abstract: Triangular fuzzy numbers are used to represent project activities whose duration times are uncertain and cannot be represented stochastically. A comprehensive path analysis method is devised using fuzzy arithmetic and a fuzzy number comparison method to determine fuzzy project completion time and the degrees of criticality of each network path. Possibility theory is then used to determine the possibilities of the project completion given the fuzzy project completion time. The existing composite method and the new comprehensive comparison method are compared and contrasted.

Probabilistic fuzzy model for dynamic systems

Abstract: We present a model, based on a fuzzy relation obtained from fuzzy referential sets on the input and output spaces, for predicting the behaviour of nonlinear dynamic systems. The model can be made to learn from experience, and the computing requirements are modest, making online application feasible. Some numerical results are compared with those of earlier models.

A new approach to handling fuzzy decision-making problems

Abstract: Techniques for handling fuzzy decision-making problems are presented in which fuzzy production rules and fuzzy set theory are used for knowledge representation. The maximum fuzzy cover generation techniques used are described in detail. Some examples are given to illustrate the maximum fuzzy cover generation process. The examples are restricted to medical diagnostic problems; however, the techniques can be applied to any other decision-making problem. >

Fuzzy non-linear programming: Theory and application in manufacturing

Abstract: Due to the complexity of the manufacturing environment, problems that can be solved by mathematical programming techniques are usually represented with non-linear programming (NP) models instead of linear programming (LP) models. When non-stochastic vagueness exists between the problem description and its corresponding NP model, fuzzy set theory can be applied to the mathematical model for the purpose of vividly representing the problem. This paper discusses the idea of the fuzzy NP model. Fuzzy set concepts are adapted to the NP objective function and constraints. An identical crisp NP model is derived from the fuzzy NP model for solving the problem numerically. Kuhn-Tucker conditions are addressed to determine the existence of a global optimal solution. A fuzzy machining economics model, which attempts to find optimal manufacturing parameters under vague elements of influence, is used to demonstrate the theory of the fuzzy NP model.

Weighted fuzzy logic and its applications

Abstract: A weighted fuzzy logic is presented in which the truth of a conjunction of propositions (or predicates) is a weighted sum of the truth of each proposition (or predicate). This is different from traditional logic where a conjunction would be false if only one component of the conjunction is false. The proposed logic is quite suitable for reasoning with incomplete knowledge and fuzzy retrieval or fuzzy matching. It is expected to have a very wide range of applications in knowledge engineering and many other areas. Expert systems in medical diagnosis and computer-aided design are considered as applications. >

FuzzyProp: a symbolic superstrate for connectionist models

Abstract: A fuzzy propositional model of categorization and pattern identification is described and then shown to be isomorphic to a nonrecurrent, semilinear connectionist system under certain common conditions. This model may be considered to be a symbolic-level description of such a connectionist system. It is argued that connectionist models need this sort of associated symbolic interpretation for both theoretical and practical reasons. >

Medical Diagnois using Simplified Multi-dimensional Fuzzy Reasoning

Abstract: This paper proposes a method for computer-assisted medical diagnosis using simplified multi-dimensional fuzzy reasoning. The reasoning is based on the iterative use of fuzzy modus ponens and fuzzy modus tollens. Seven types of easy-to-gather production rules for fuzzy medical diagnosis are proposed. The fuzzy inference mechanism accepts patient's symptoms with attached degree of compatibility and infers diagnoses with a degree of certainty that expresses the degree to which it can be certain that the diagnosis is correct. Truthfulness of relationships between symptoms and diseases are fuzzily expressed in terms of linguistic truth values such as "Very True" and "Possibly True". Practical methods for the examination of unconfirmed symptoms and for the reexamination of given symptoms are also considered.

The Analysis Of Fuzzy Dynamic Systems Using Cell-to-cell Mapping

Abstract: Since Zadeh's first paper about fuzzy control, many developments have been done in this area. Many researchers tried to create a well structured methodology for building and analyzing fuzzy control systems. However, the efforts are more successful on the applicational aspect than the theoretical one. There are especially few satisfactory results in stability analysis of fuzzy control systems because of the great uncertainty and nonlinearity involved. In this paper, a Cell- to-Cell Mapping method which was successfully used in the nonlinear system analysis is applied to analyze a fuzzy dynamic system. By first partitioning a state - to a Cell State Space, a fuzzy mapping is transfted into a cell-to-cell mapping. Then, an algorithin is used to locate the periodic solutions of the fuzzy dynamic system. The Domain of Attraction of a periodic motion is also shown to describe the behavior of the fuzzy dynamic system.

On Generating Linguistic Rules for Fuzzy Models

Abstract: This paper proposes a method for generating linguistic rules based on fuzzy reasoning with a collection of fuzzy or nonfuzzy dataNumerical examples are presented to show the efficiency of this method

On fuzzy cliques in fuzzy networks

Abstract: A definition of fuzzy clique in social networks is suggested which overcomes five limitations of current definitions. This definition is based on the networks in which the 0–1 strengths, the weighted strengths, and fuzzy strengths are all allowed. The fuzzy distance in such a network is defined. The node‐clique and clique‐clique coefficients are suggested. The core and the periphery of fuzzy cliques are discussed formally. A “cone like” property of the cores is discovered. The network structures are discussed using the new definition. A “no circle” property of networks is found. Basic fuzzy tools and the related algorithms are also discussed. Some examples are analyzed to demonstrate the theory.

A new approach for the global analysis of fuzzy dynamical systems

Abstract: A novel approach for analyzing the global behavior of a fuzzy dynamical system is presented. It applies the concept and method of the cell-to-cell mapping to obtain the evolving trend of the states of the fuzzy dynamical system. The behavior of system is characterized by equilibria, periodic motions, and their domain of attractions. The min-max operation accumulates the fuzziness of a fuzzy system in every iteration and makes state evolution obscure. The proposed method transforms a given fuzzy mapping to a Z-to-Z mapping and does not accumulate fuzziness. Both the real and fuzzy initial state response analyses are discussed. An inverted pendulum controlled by a fuzzy controller is analyzed by the method to illustrate its validity. >

Image analysis using iconic fuzzy sets

Abstract: A formalism is presented for the iconic representation of knowledge in a semantic net. Nodes are represented as fuzzy sets, relations as functions over fuzzy sets. Moreover, the author demonstrates a consistent framework for the transition from the symbolic to the iconic level. He also defines a procedure for the explication, fuzzyfication and operationalization of natural language description predicates. This concept has successfully been used for a model-driven image analysis approach in the domain of MRI (magnetic resonance imaging) brain slices. The concept also has the potential for a data-driven approach. Measurements of features of fuzzy image structures can be represented in the semantic net as fuzzy assertions. Possible faults (pathologies) can be incorporated by using information form other sources (neurological studies) as (fuzzy) assertions. >