Tuning membership functions in local evolutionary learning of fuzzy rule bases

Abstract

The local evolutionary generation of fuzzy rule bases employs independent searches in local regions throughout the input space and combines the local results to produce a global model. The paper presents a rule base tuning strategy that is compatible with the local evolutionary generation of fuzzy rule bases. Rule base tuning is accomplished by modifying the decomposition of the input domain based on the distribution and values of the training data. A local tuning algorithm must maintain a correspondence between competing rules in the population. An experimental suite has been developed to exhibit the potential for model optimization using rule base tuning. of particular interest is the ability of rule base tuning to compensate for the effects of sparse data.