Volterra Series identification Based on State Transition Algorithm with Orthogonal Transformation
Abstract: A Volterra kernel
identification method based on state transition algorithm with orthogonal
transformation (called OTSTA) was proposed to solve the hard problem in
identifying Volterra kernels of nonlinear systems. Firstly, the population with
chaotic sequences was initialized by using chaotic strategy. Then the
orthogonal transformation was used to finish the mutation operator of the
selected individual. OTSTA was used on the identification of Volterra series,
and compared with particle swarm optimization (called PSO) and state transition
algorithm (STA). The simulation results showed that OTSTA has better identification
precision and convergence than PSO and STA under non-noise interference. And
when there is noise, the identification precision, convergence and
anti-interference of OTSTA are also superior to PSO and STA.
Keywords: State Translation
Algorithm; Orthogonal Transformation; Nonlinear System; Volterra Series; System
Identification
Author: Cong Wang, Hong-Li
Zhang, Wen-hui Fan
Journal Code: jptkomputergg160201
