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

Authors:
Babita Majhi;G. Panda;B. Mulgrew
Affiliations:
Department of Electronics & Communication Engineering, National Institute of Technology, Rourkela, Orissa, India;Department of Electronics & Communication Engineering, National Institute of Technology, Rourkela, Orissa, India;Institute of Digital Communication, University of Edinburgh, UK
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 4
Cited 1

Wireless sensor networks: a survey

Computer Networks: The International Journal of Computer and Telecommunications Networking
An elitist distributed particle swarm algorithm for RF IC optimization

Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

IEEE Transactions on Signal Processing - Part II
Incremental Adaptive Strategies Over Distributed Networks

IEEE Transactions on Signal Processing

Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.