Redundant fuzzy rules exclusion by genetic algorithms

Lekova, Anna; Mikhailov, L.; Boyadjiev, D.; Nabout, A.

doi:10.1016/s0165-0114(97)00056-0

Cited by 25 publications

(8 citation statements)

References 10 publications

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“…They become the potential redundant rules in fuzzy system. Lekova et al (1998) applied GAs to exclude those redundant rules from a fuzzy system.…”

Section: Evolutionary Fuzzy Controlmentioning

confidence: 99%

Intelligent Control

Siddique¹

2014

Studies in Computational Intelligence

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The series ''Studies in Computational Intelligence'' (SCI) publishes new developments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.

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“…They become the potential redundant rules in fuzzy system. Lekova et al (1998) applied GAs to exclude those redundant rules from a fuzzy system.…”

Section: Evolutionary Fuzzy Controlmentioning

confidence: 99%

Intelligent Control

Siddique¹

2014

Studies in Computational Intelligence

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“…To make the fuzzy rules more sufficient and reliable, no matter those extracted by certain automatic algorithm or those obtained from human experts, etc., all of them will be adopted to support the performance modeling. In this case, there must exist many unexpected and undesired conflicts and redundancies between those rules [10]. Thus before applying these fuzzy rules, a theorem with logical criteria is suggested for partial conflict and redundancy validation of them that are necessary for our method.…”

Section: The Preparative Research For the Application Of Fuzzy Rulesmentioning

confidence: 99%

Modeling for Investment Guidance as a Performance Pre-assessment Method of Enterprise Integration

Zhou

et al. 2006

2006 10th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW'06)

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Current enterprise performance research focuses on the post-assessment and is always lack of approaches about the necessary pre-assessment. In this paper a modeling method for investment guidance of enterprise integration is proposed, which is a significant pre-assessment method. Because of the numerous dynamic and subjective indicators of performance, modeling method presented in this paper is mainly based on the fuzzy rule for its natural advantages in this area. Firstly, considering the research requirements discussed in the literature review, the performance modeling framework for investment guidance is put forward grounded on the process model. Secondly, the original motivation to employ the fuzzy logic method is discussed and a theorem with logical criteria is suggested for conflict and redundancy validation of fuzzy rules which is necessary for performance modeling. Finally, the model for investment guidance is constructed and then a non-linear programming model is formulated to optimize the investment of enterprise integration.

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“…However, FLCs have a drawback of finding its knowledge base which is based on a tedious and unreliable trial and error process. To overcome this drawback, one can use supervised learning [7][8][9][10][11] that needs a teacher or input/output training data. However, in many practical cases the model is totally or partially unknown and it is difficult or expensive and in some cases impossible to get training data.…”

Section: Introductionmentioning

confidence: 99%

Q(λ)-learning adaptive fuzzy logic controllers for pursuit-evasion differential games

Desouky

Schwartz

2011

Int. J. Adapt. Control Signal Process.

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This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q( )-learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to three different pursuit-evasion differential games. The proposed technique is compared with the classical control strategy, Q( )-learning only, and the technique proposed by Dai et al. (2005) in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique.

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Redundant fuzzy rules exclusion by genetic algorithms

Cited by 25 publications

References 10 publications

Intelligent Control

Intelligent Control

Modeling for Investment Guidance as a Performance Pre-assessment Method of Enterprise Integration

Q(λ)-learning adaptive fuzzy logic controllers for pursuit-evasion differential games

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