Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Fast learning in networks of locally-tuned processing units
Neural Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Improving performance of radial basis function network based with particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
International Journal of Bio-Inspired Computation
A radial basis function redesigned for predicting a welding process
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Analysis and evaluation in a welding process applying a Redesigned Radial Basis Function
Expert Systems with Applications: An International Journal
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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.