Use of an Evolutionary Inductive Self-Organizing Network for Uncertain Nonlinear and Robotic Systems

Abstract

We discuss a new design methodology for an inductive self-organizing network using an evolutionary algorithm and its practical applications. The inductive self-organizing network centres on the idea of a group method for data handling. The performances of this network depend strongly on the number of input variables available to the model and the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must be used with its heavy computation burden and low efficiency. Moreover it does not guarantee that the obtained model is the best one. In this paper, we propose an evolutionary inductive self-organizing network to alleviate these problems. The order of the polynomial, the number of input variables and the optimum input variables are encoded as a chromosome and the fitness of each chromosome is computed. So the appropriate information for each node is evolved accordingly and tuned gradually throughout the genetic iterations. We can show that the proposed model is a sophisticated and versatile architecture which can construct models for limited data sets, as well as heavy complex robotic systems.