This paper presents an evolutionary learning algorithm to facilitate the design of fuzzy controllers for mobile robots. It discusses the concepts, feasibility, bene ts and limitations of current evolutionary techniques for fuzzy rule discovery and tuning. We propose an evolution strategy that optimizes the gain factors in the conclusion part of TakagiSugeno-Kang type fuzzy rules. We describe tw...

In maintenance field, prognostic is recognized as a key feature as the estimation of the remaining useful life of an equipment allows avoiding inopportune maintenance spending. However, it can be difficult to define and implement an adequate and efficient prognostic tool that includes the inherent uncertainty of the prognostic process. Within this frame, neuro-fuzzy systems are well suited for ...

In recent years the use of Genetic or Evolutionary techniques has produced interesting results on the automatic generation of Knowledge Bases for Fuzzy Logic Controllers. Three diierent representations of the rule base have been considered: list of rules, relational matrix and decision table. The use of lists of rules reduces the dimension of the rule base but presents some handicaps for crosso...

A fuzzy-controller design by the hybrid of genetic algorithm and particle-swarm optimisation (F-HGAPSO) is employed for a thyristor-controlled series capacitor (TCSC) to improve the transient stability of flexible AC transmission systems (FACTS). According to the variation of rotation speed, a fuzzy controller decides an approximate series capacitance to achieve a better dynamic response of FAC...

In this paper, numerical exemplars are used in a training method to find the structure of a fuzzy-neural network. After this structure learning, a genetic algorithm is applied to determine the initial weights of the neural network, thereby guiding the neural network to a nearoptimal initialization. These well-initialized networks are then trained with backpropagation algorithm. Using this propo...

Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute. Then, a multiobjective evolutionary algorithm is applied to perform fuzzy rule selection. Since using multiple granularities for the same attribute has been sometim...