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Showing papers on "Fuzzy logic published in 1991"


Journal ArticleDOI
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
Abstract: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed. >

3,237 citations


Journal ArticleDOI
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about concrete mechanical properties such as E-modulus and compressive strength.

1,746 citations



Journal ArticleDOI
TL;DR: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.
Abstract: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

1,476 citations


Proceedings ArticleDOI
01 Aug 1991
TL;DR: A control-theoretic approach to reactive flow control in networks that do not reserve bandwidth is presented, and a technique to extract and use additional information from the system to develop a continuous-time system model is presented.
Abstract: This paper presents a control-theoretic approach to reactive flow control in networks that do not reserve bandwidth. We assume a round-robin-like queue service discipline in the output queues of the network's switches, and propose deterministic and stochastic models for a single conversation in a network of such switches. These models motivate the Packet-Pair rate probing technique, and a provably stable rate-based flow control scheme. A Kalman state estimator is derived from discrete-time state space analysis, but there are difficulties in using the estimator in practice. These difficulties are overcome by a novel estimation scheme based on fuzzy logic. We then present a technique to extract and use additional information from the system to develop a continuous-time system model. This is used to design a variant of the control law that is also provably stable, and, in addition, takes control action as rapidly as possible. Finally, practical issues such as correcting parameter drift and coordination with window flow control are described.

790 citations


Journal ArticleDOI
TL;DR: The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis, and its ability to detect ‘good’ clusters amongst noisy data is demonstrated.

722 citations


Journal ArticleDOI
TL;DR: It is shown from two examples that the successive identification method of a fuzzy model is very useful for modeling complex systems.

485 citations


Proceedings Article
14 Jul 1991
TL;DR: It is concluded that the proposedl new fuzzy inference system can not only incorporate prior knowledge about the original system but also fine-tune the membership functions of the fuzzy rules as the training data set varies.
Abstract: We propose a new approach to build a, fuzzy inference system of which the parameters can be updated to achieve a desired input-output mapping. The structure of the proposed fuzzy inference system is called generalized neural networks, and its learning procedure (rules to update parameters) is basically composed of a gradient descent algorithm and Kalman filter algorithm. Specifically, we first introduce the concept of generalized neural networks (GNN's) and develop a gradient-descent-based supervised learning procedure to update the GNN's parameters. Secondly, we observe that if the overall output of a GNN is a linear combination of some of its parameters, then these parameters can be identified by one-time application of Kalman filter algorithm to minimize the squared error, According to the simulation results, it is concluded that the proposedl new fuzzy inference system can not only incorporate prior knowledge about the original system but also fine-tune the membership functions of the fuzzy rules as the training data set varies.

426 citations



Journal ArticleDOI
TL;DR: The T-operators are extended to the conventional fuzzy reasoning methods which are based on the min and max operators and provide both a general and a flexible method for the design of fuzzy logic controllers and, more generally, for the modelling of any decision-making process.

364 citations



Journal ArticleDOI
Ronald R. Yager1
TL;DR: The concept of linguistic quantifiers is introduced and some applications of these structures are looked at, particularly concerned with their application to linguistic summarizers, syllogistic reasoning and multi-criteria decision making.

Journal ArticleDOI
TL;DR: The relations between the similarity-based interpretation of the role of conditional possibility distributions and the approximate inferential procedures of Baldwin are discussed and a straightforward extension of the theory to the case where the similarity scale is symbolic rather than numeric is described.

Journal ArticleDOI
TL;DR: This second part of an overview of fuzzy set-based methods for approximate reasoning is devoted to deductive approaches dealing with knowledge bases made of a collection of symbolic expressions to which numerical weigths are attached.

Journal ArticleDOI
TL;DR: The concepts of fuzzy continuity, product and quotient spaces are presented, and their fundamental properties are obtained in fuzzifying topology.

Journal ArticleDOI
TL;DR: The binary-state assumption is reserved and the possibility assumption is taken in place of the probability assumption, and a theory of fuzzy reliability (i.e., posbist reliability theory) is established.

Journal ArticleDOI
TL;DR: By utilizing this decision algorithm, the decision-makers’ fuzzy assessments with various rating attitudes and the trade-off among various selection criteria can be taken into account in the aggregation process to asssure more convincing and accurate decision-making.
Abstract: In this paper, a facility site selection algorithm is proposed. The algorithm based on the concepts of fuzzy set theory and the hierarchical structure analysis to aggregate decision-makers’ linguistic assessments about criteria weightings and the suitability of facility sites versus various selection criteria to obtain fuzzy suitability indices. Then rank the suitability ratings to determine the best facility site selection. By utilizing this decision algorithm, the decision-makers’ fuzzy assessments with various rating attitudes and the trade-off among various selection criteria can be taken into account in the aggregation process to asssure more convincing and accurate decision-making.

Journal ArticleDOI
TL;DR: A new approach using fuzzy dynamic programming is proposed for the unit commitment of a power system, in which the hourly loads, the cost, and system security are all expressed in fuzzy set notations is developed.
Abstract: A new approach using fuzzy dynamic programming is proposed for the unit commitment of a power system. A characteristic feature of the approach is that the errors in the forecast hourly loads can be taken into account by using fuzzy set notations, making the approach superior to the conventional dynamic programming method which assumes that the hourly loads are exactly known and there exist no errors in the forecast loads. To reach an optimal commitment strategy under the fuzzy environment, a fuzzy dynamic programming model in which the hourly loads, the cost, and system security are all expressed in fuzzy set notations is developed. The effectiveness of the approach is demonstrated by the unit commitment of the Taiwan power system, which contains six nuclear units, 48 thermal units, and 44 hydro units. >

Journal ArticleDOI
TL;DR: It is suggested that the parameter in the BADD family, the distinction between different defuzzification methods, is related to the confidence the authors have in the rest of the controller, and the use of these BADD transformations form the basis of a learning scheme to obtain the optimaldefuzzification method in a given application.
Abstract: Defuzzification in fuzzy logic controllers concerns itself with the issue of selecting an appropriate crisp value from the fuzzy output of the controller. We provide a parametized family of defuzzification operations. We call this family BAsic Defuzzification Distributions (BADD). We show that the commonly used methods. Mean of Maximum and Center of Area are special cases of this family. We suggest the use of these BADD transformations form the basis of a learning scheme to obtain the optimal defuzzification method in a given application. We suggest that the parameter in the BADD family, the distinction between different defuzzification methods, is related to the confidence we have in the rest of the controller.

Proceedings ArticleDOI
20 May 1991
TL;DR: Fuzzy time has proven to be highly effective against the timing channels in the VAX security kernel, and does so at a much lower-than-anticipated performance cost.
Abstract: Fuzzy time is a collection of techniques that reduces the bandwidths of covert timing channels by making all clocks available to a process noisy. Developed in response to the problems posed by high-speed hardware timing channels, fuzzy time has been implemented in the VAX security kernel. Fuzzy time has proven to be highly effective against the timing channels in the VAX security kernel. Not only does fuzzy time close the high-speed channels, it does so at a much lower-than-anticipated performance cost. It is believed that the VAX security kernal managed to meet the covert channel guidelines while maintaining a good balance between security and performance. >

Journal ArticleDOI
TL;DR: The results support that the class of weak t-norms having the Exchange Property seems to be a good model of the conjunction (or equivalently, of intersection) operator in fuzzy set theory.

Proceedings ArticleDOI
28 Sep 1991
TL;DR: In this article, the application of fuzzy logic in a speed control system that uses a phase-controlled bridge converter and a separately excited DC machine is described. But the simulation study indicates the superiority of fuzzy control over the conventional control methods.
Abstract: A description is presented of the application of fuzzy logic in a speed control system that uses a phase-controlled bridge converter and a separately excited DC machine. The fuzzy control is used to linearize the transfer characteristics of the converter in the discontinuous conduction mode occurring at light load and/or high speed. The fuzzy control is then extended to the current and speed control loops replacing the conventional proportional-integral (PI) control method. The compensation and control algorithms have been developed in detail and verified by digital simulation of a drive system. The simulation study indicates the superiority of fuzzy control over the conventional control methods. Fuzzy logic seems to have a lot of promise in the application of power electronics. >

Journal ArticleDOI
Yufei Yuan1
TL;DR: Four criteria for evaluating fuzzy ranking methods are investigated: fuzzy preference representation, rationality of fuzzy ordering, distinguishability, and robustness; a new, improved ranking method is suggested.

Journal ArticleDOI
TL;DR: It is argued that the previous method of solving for x, based on the extension principle and regular fuzzy arithmetic, should be abandoned since it too often fails to produce a solution.

Journal ArticleDOI
TL;DR: This paper presents what it believes to be a straightforward and computationally efficient procedure for dealing with the FLP problem with any general class of nonlinear membership functions.

Journal ArticleDOI
TL;DR: In this article, a fuzzy model for power system operation is presented, where uncertainty in loads and generations are modeled as fuzzy numbers and system behavior under known (while uncertain) injections is dealt with by a DC fuzzy power flow model.
Abstract: A fuzzy model for power system operation is presented. Uncertainties in loads and generations are modeled as fuzzy numbers. System behavior under known (while uncertain) injections is dealt with by a DC fuzzy power flow model. System optimal (while uncertain) operation is calculated with linear programming procedures in which the problem nature and structure allow some efficient techniques such as Dantzig-Wolfe decomposition and dual simplex to be used. Among the results, one obtains a fuzzy cost value for system operation and possibility distributions for branch power flows and power generations. Some risk analysis is possible, as system robustness and exposure indices can be derived and hedging policies can be investigated. >

Journal ArticleDOI
TL;DR: The quadratic membership functions as defined by A. Celmiņs are considered to propose an identification method of interactive fuzzy parameters in possibilistic linear systems and can be reduced to linear programming, so that it is very easy to obtain the possibillistic distribution of parameters.

Journal ArticleDOI
TL;DR: In the new method, compound queries composed of keywords with and, or and/or not are processed, and the learning method has been modified to allow fuzzy judgements as well as compound queries.

Journal ArticleDOI
TL;DR: In this article, the application of a fuzzy logic controller to improve the stability of electric power systems is presented, where the stabilizing signal is computed using the standard fuzzy membership function depending on the speed acceleration state of the generator in the phase plane.
Abstract: The application of a fuzzy logic controller to improve the stability of electric power systems is presented. The stabilizing signal is computed using the standard fuzzy membership function depending on the speed acceleration state of the generator in the phase plane. The required measurement is the speed deviation over two samples. The effectiveness of the proposed stabilizer is demonstrated by simulation studies for different operating conditions and disturbances. >

Proceedings Article
John Yen1
24 Aug 1991
TL;DR: The generalized knowledge representation framework not only alleviates the difficulty of conventional AI knowledge representation schemes in handling imprecise and vague information, but also extends the application of fuzzy logic to complex intelligent systems that need to perform highlevel analyses using conceptual abstractions.
Abstract: During the past decade, knowledge representation research in AI has generated a class of languages called term subsumption languages (TSL), which is a knowledge representation formalism with a well-defined logic-based semantics-Due to its formal semantics, a term subsumption system can automatically infer the subsumption relationships between concepts defined in the system. However, these systems are very limited in handling vague concepts in the knowledge base. In contrast, fuzzy logic directly deals with the notion of vagueness and imprecision using fuzzy predicates, fuzzy quantifiers, linguistic variables, and other constructs. Hence, fuzzy logic offers an appealing foundation for generalizing the semantics of term subsumption languages. Based on a test score semantics in fuzzy logic, this paper first generalizes the semantics of term subsumption languages. Then, we discuss impacts of such a generalization to the reasoning capabilities of term subsumption systems. The generalized knowledge representation framework not only alleviates the difficulty of conventional AI knowledge representation schemes in handling imprecise and vague information, but also extends the application of fuzzy logic to complex intelligent systems that need to perform highlevel analyses using conceptual abstractions.