Training data reduction for optimisation of fuzzy logic systems for dynamic modeling of robot manipulators by genetic algorithms

The paper reports a novel method for the choice and reduction of the training data set for dynamic modeling of robotic manipulators (RMs) by fuzzy logic systems (FLSs) that are evolved by a genetic algorithm (GA). A multi-population, multi-objective GA is used for structure evolution and optimisation of the FLSs and constants for the precise approximation of the dynamic model (DM) and the simplicity of the FLSs and the complete DM. The initial large set of training data is considerably reduced without decreasing its representative quality.