Identification of Nolinear Systems Using Inceremnetal and Diffusion Differential Evolution Algorithms

Abstract

The paper investigates on the use of Differential Evolution (DE) for training the system identification model particularly when the measurement data are available at different sensor nodes. Under such situation the conventional DE algorithms cannot be applied directly. Hence in this paper two distributed learning algorithms known as incremental DE (IDE) and diffusion DE (DDE) have been developed to meet the requirements. The identification of nonlinear plants under different noise conditions has been obtained through simulation study and the results have been compared with distributed PSO algorithms. The performance of the proposed algorithms in terms of convergence rate and minimum mean squared error indicate that the distributed DE algorithms exhibit superior performance compared to its PSO counter parts.