Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence
(AI) methods to build comprehensive AI systems. Describes major AI problems (pattern …

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal
calls for the design of TRFN by either neural network or genetic algorithms depending on the …

The idea about this book has evolved during the process of its preparation as some of the
results have been achieved in parallel with its writing. One reason for this is that in this area …

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is
proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon …

The analysis and control of complex systems have been the main motivation for the
emergence of fuzzy set theory since its inception. It is also a major research field where …

A recursive approach for adaptation of fuzzy rule-based model structure has been
developed and tested. It uses on-line clustering of the input–output data with a recursively …

A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described
in the paper. The encoding procedure is applied to the problem of automatically generating …

In this paper, we address the issue of ordering exams by simultaneously considering two
separate heuristics using fuzzy methods. Combinations of two of the following three heuristic …

In this paper, we describe an approach for solving the quadratic assignment problem (QAP)
that is based on genetic algorithms (GA). It will be shown that a standard canonical GA …

Particle swarm optimization (PSO) is a powerful optimization technique that has been
applied to solve a number of complex optimization problems. One such optimization …