An extended self-organizing map for nonlinear system identification

Local model networks (LMN) are recently proposed for modeling a nonlinear d ynamical system with a set of locally valid submodels across the operating space. Despite the recent advances of LMN, a priori knowledge of the proces ses has to be exploited for the determination of the LMN structure and the weighting functions. However, in most practical cases, a priori knowledge m ay not be readily accessible for the construction of LMN. In this paper, an extended self-organizing map (ESOM) network, which can overcome the aforem entioned difficulties, is developed to construct the LMN. The ESOM is a mul tilayered network that integrates the basic elements of a traditional self- organizing map and a feed-forward network into a connectionist structure. A two-phase learning algorithm is introduced for constructing the ESOM from the plant input-output data, with which the structure is determined through the self-organizing phase and the model parameters are obtained by the lin ear least-squares optimization method. Literature examples are used to demo nstrate the effectiveness of the proposed scheme.