Fuzzy rule base identification using an incremental approach

Abstract

A sparse fuzzy rule base provides low complexity and low memory requirements for the fuzzy system. Automatic fuzzy model generation from sample data involves two main tasks. These are structure determination and parameter identification. In this paper, we present a new approach that initially generates two rules, then gradually adds new rules to the rule base, and then finds the quasi-optimal values of the parameters using particle swarm optimization.