Robust identification of nonlinear objects with the help of an evolving radial basis network

The problem of neural network-based robust identification of nonlinear dynamic objects in the presence of non-Gaussian noise is considered. To solve this problem, a radial basis network was chosen whose structure is specified and training is provided with the help of a genetic algorithm. The simulat...

Full description

Saved in:
Bibliographic Details
Published in:Cybernetics and systems analysis 2013-03, Vol.49 (2), p.173-182
Main Authors: Rudenko, O. G., Bezsonov, O. O., Rudenko, S. O.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Approximation
Artificial Intelligence
Computer simulation
Control
Cybernetics
Dynamical systems
Efficiency
Evolution
Genetic algorithms
Identification
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Networks
Neural networks
Neurons
Noise
Processor Architectures
Robustness
Simulation
Software Engineering/Programming and Operating Systems
Studies
Systems Theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of neural network-based robust identification of nonlinear dynamic objects in the presence of non-Gaussian noise is considered. To solve this problem, a radial basis network was chosen whose structure is specified and training is provided with the help of a genetic algorithm. The simulation results are presented that confirm the efficiency of the proposed approach.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-013-9497-0