On the optimization of fuzzy systems using bio-inspired strategies
José Valente de Oliveira · 2002
The optimization of fuzzy systems using bio-inspired strategies, such as neural network learning rules or evolutionary optimization techniques, is becoming more and more popular. In general, fuzzy systems optimized in such a way cannot provide a linguistic interpretation, preventing us from using one of their most interesting and useful features. This work addresses this difficulty and present a design methodology to overcome it. A set of properties that obviate the subjective task of interpreting linguistically fuzzy systems is provided. These properties are translated in terms of nonlinear constraints that are coded within a given optimization scheme, such as backpropagation. Illustrative numerical examples are also included.