Training RBF Neural Network Via Quantum-Behaved Particle Swarm Optimization

Radial Basis Function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. The existing training algorithms, such as Orthogonal Least Squares (OLS) algorithm, clustering and gradient descent algorithm, have their own shortcomings. In this paper, we make an attempt to explore the applicability of Quantum-behaved Particle Swarm Optimization, a newly proposed evolutionary search technique, in training RBF neural network. The proposed QPSO-Trained RBF network was test on nonlinear system identification problem, and the results show that it can identifying the system more quickly and precisely than that trained by Particle Swarm algorithm.

[1]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[2]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[3]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[6]  J. Kennedy.  Stereotyping: improving particle swarm performance with cluster analysis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[7]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[8]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[9]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[10]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[11]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[16]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[17]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[18]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[19]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).