Volterra system-based neural network modeling by particle swarm optimization approach

Authors:
Yi-Sung Yang;Wei-Der Chang;Teh-Lu Liao
Affiliations:
Department of Engineering Science, National Cheng Kung University Tainan 701, Taiwan.;Department of Computer and Communication, Shu-Te University Kaohsiung 824, Taiwan.;Department of Engineering Science, National Cheng Kung University Tainan 701, Taiwan.
Venue:
Neurocomputing
Year:
2012

Citing 13
Cited 0

Neural network design

Neural network design
ARMA model parameter estimation based on the equivalent MA approach

Digital Signal Processing
Soft-computing techniques and ARMA model for time series prediction

Neurocomputing
Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systems

Neurocomputing
Identification and control of nonlinear systems by a time-delay recurrent neural network

Neurocomputing
Nonlinear system identification via Laguerre network based fuzzy systems

Fuzzy Sets and Systems
A novel adaptive bilinear filter based on pipelined architecture

Digital Signal Processing
A modified gradient-based neuro-fuzzy learning algorithm and its convergence

Information Sciences: an International Journal
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization

Engineering Applications of Artificial Intelligence
Minimum Mean-Square Error Equalization for Second-Order Volterra Systems

IEEE Transactions on Signal Processing - Part I
Brief Optimal expansions of discrete-time Volterra models using Laguerre functions

Automatica (Journal of IFAC)
Time series AR modeling with missing observations based on the polynomial transformation

Mathematical and Computer Modelling: An International Journal
Identification and control of dynamical systems using neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a novel identification method for nonlinear discrete dynamic systems. A feedforward neural network with the structure of Volterra system is newly presented. This kind of mathematical model possesses more adjustable parameters than the original Volterra system to further enhance the modeling capacity. Tuning adjustable parameters inside the neural network is based on the particle swarm optimization (PSO) instead of the commonly used back-propagation method. The PSO algorithm is with multiple direction searches and can easily find out the global solution for the given optimization problem. This paper also develops the whole design steps for PSO-based feedforward neural network modeling for nonlinear discrete systems. Two kinds of examples are illustrated to validate the feasibility and efficiency of the proposed method. In addition, some examinations containing different initial conditions and population sizes, and presence of measurement noises are considered to evaluate the modeling performance.