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Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems: Design and Development
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Author(s): Masoud Mohammadian (Faculty of Business Government and Law, University of Canberra, Canberra, Australia)
Copyright: 2017
Volume: 6
Issue: 3
Pages: 19
Source title:
International Journal of Fuzzy System Applications (IJFSA)
Editor(s)-in-Chief: Deng-Feng Li (School of Management and Economics, University of Electronic Science and Technology of China (UESTC))
DOI: 10.4018/IJFSA.2017070105
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Abstract
Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.
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