NDLI: Nonlinear System Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	dos Santos Coelho, L. Krohling, R.A.
Copyright Year	2006
Description	Author affiliation: Pontifical Catholic Univ. of Parana, Curitiba (dos Santos Coelho, L.)
Abstract	Artificial neural networks, in particular, feedforward multilayer networks and basis function networks, have gradually established themselves as a usual tool in approximating complex nonlinear systems. B-spline networks, a type of basis function neural network, are normally trained by gradient-based methods, which may fall into local minima during the learning phase. In order to overcome the drawbacks encountered by conventional learning methods, particle swarm optimization - a swarm intelligence methodology - can provide a stochastic global search of B-spline networks for nonlinear system identification. In this paper, a modified particle swarm optimization algorithm using Gaussian and Cauchy probability distributions are applied to adjust the control points of B-spline neural networks. Simulation results for the identification of Rossler systems are provided and demonstrate the effectiveness and robustness of the proposed identification scheme.
Starting Page	3748
Ending Page	3753
File Size	264881
Page Count	6
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247392
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Spline Neural networks Particle swarm optimization Artificial neural networks Multi-layer neural network Learning systems Stochastic systems Probability distribution Robustness
Content Type	Text
Resource Type	Article

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