Authors:
S. Z. Rizvi
and
H. N. Al-Duwaish
Affiliation:
King Fahd Univ. of Petroleum & Minerals, Saudi Arabia
Keyword(s):
Particle swarm optimization, Neural network training, Subspace identification, Static nonlinearity, Dynamic linearity, Radial basis function (RBF) neural networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
This paper presents a new method for modeling of Hammerstein systems. The developed identification method uses state-space model in cascade with radial basis function (RBF) neural network. A recursive algorithm is developed for estimating neural network synaptic weights and parameters of the state-space model. No assumption on the structure of nonlinearity ismade. The proposed algorithm works under the weak assumption of richness of inputs. The problem of modeling is solved as an optimization problem and Particle Swarm Optimization (PSO) is used for neural network training. Performance of the algorithm is evaluated in the presence of noisy data and Monte-Carlo simulations are performed to ensure reliability and repeatability of the identification technique.