EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method
Fuzzy Sets and Systems
Design of t–s fuzzy classifier via linear matrix inequality approach
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
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This paper presents an approach to identify a fuzzy control model for determining an economical running pattern for a high-speed railway through an optimal compromise between trip time and energy consumption. Since the linguistic model is intuitive and informative to railway operators, they can easily implement a control strategy for saving energy. The approach includes structure identification and parameter identification. It is proposed to utilize a fuzzy c-means clustering and a GA hybrid scheme to identify the structure and parameters of a fuzzy model, respectively. To evaluate the advantages and the effectiveness of the suggested approach, numerical examples are presented. Comparison shows that the proposed approach can produce a fuzzy model with higher accuracy and smaller number of rules than previously achieved in other works. To show the global optimization and local convergence of the GA hybrid-scheme, an optimization problem having a few local minima and maxima is considered