An Accurate COG Defuzzifier Design Using the Coadaptation of Learning and Evolution

Abstract

This paper proposes an accurate center of gravity (COG) defuzzification method that improves both the system's approximation ability and the control performance of a fuzzy logic controller. The accuracy of the proposed COG defuzzifier is obtained by representing the output membership functions (MFs) with various design parameters such as the centers, widths, and modifiers of MFs and by adjusting these design parameters with Lamarckian co-adaptation of learning and evolution, where the learning performs a local search of design parameters in an individual COG defuzzifier, but the evolution performs a global search of design parameters among a population of various COG defuzzifiers. This co-adaptation scheme allows it to evolve much faster than the nonlearning case and gives a higher possibility of finding an optimal solution due to its wider searching capability. An application to the truck backer-upper control problem of the proposed co-adaptive design method of COG defuzzifier is presented.