Two-Dimensional (2D) particle swarms for structure selection of nonlinear systems

Abstract

The present study proposes a new structure selection approach for non-linear system identification based on Two-Dimensional particle swarms (2D-UPSO). The 2D learning framework essentially extends the learning dimension of the conventional particle swarms and explicitly incorporates information about the cardinality (i.e., number of terms) into the search process. This property of the 2D-UPSO is exploited to determine the correct structure of the non-linear systems. The efficacy of the proposed approach is demonstrated by considering several simulated benchmark nonlinear systems in discrete and continuous domains. In addition, the proposed approach is applied to identify a parsimonious structure from practical non-linear wave-force data. The results of the comparative investigation with four meta-heuristic algorithms and classical orthogonal forward regression methods illustrate that the proposed 2D-UPSO can successfully detect the correct structure of the non-linear systems.