Distributed identification of nonlinear processes using incremental and diffusion type PSO algorithms

This paper introduces two new distributed learning algorithms : Incremental Particle Swarm Optimization (IPSO) and Diffusion Particle Swarm Optimization (DPSO). These algorithms are applied for distributed identification of nonlinear processes using cooperation among adaptive nodes. Identification of four standard nonlinear plants have been carried out through simulation to assess the performance of these algorithms. The results indicate better or identical identification performance offered by the proposed distributed algorithms compared to that offered by the conventional PSO based algorithm. The improvement is observed in terms of CPU time, accuracy in response matching and speed of convergence.