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Development of a fuzzy system design strategy using evolutionary computation
| Title: | Development of a fuzzy system design strategy using evolutionary computation |
| Author: | Bush, Brian O. |
| Description: | Natural language is perhaps the most powerful form of conveying information for any given problem or situation. Combining natural language and numerical information into fuzzy systems provide the framework to represent knowledge, constraints and inference procedures. Fuzzy systems are advantageous in the development of systems solutions that perform tasks such as automatic modeling, prediction, pattern recognition, and optimal decision making, control and planning. Thus, fuzzy systems are an essential tool for industrial and manufacturing systems engineering. Traditionally fuzzy system design is carried out by a time-consuming trial-and-error process, in order to incorporate expert knowledge into a fuzzy model. To date there is no systematic procedure to design fuzzy systems that has been met with wide-acceptance and usage. In this research, a formal methodology has been developed for the design and adaptation of effective fuzzy systems displaying both robustness and computational efficiency. The goal was to improve upon a functional fuzzy system by modifying the fuzzy sets of fuzzy rules using evolutionary computation principles. An experimental implementation was developed. The relative influence of selected factors such mutations and crossover schemes, mutations rates, and fuzzy rule structure upon the methodology's effectiveness within the experimental context were also addressed. Finally, the application of the methodology to function approximation, classification, regression, and the prediction of chaotic dynamics were accomplished to demonstrate the computational characteristics, complexity and inherent strength of the developed methodology. |
| Permanent Link: |
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1178656308
http://hdl.handle.net/2374.OX/14627 |
| Date: | 1996 |
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