A Novel Elliptical Basis Function Neural Networks Optimized by Particle Swarm Optimization
In this paper, a novel model of elliptical basis function neural networks (EBFNN) is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. Finally, the experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.
KeywordsParticle Swarm Optimization Radial Basis Function Neural Network Hybrid Particle Swarm Optimization Good Generalization Performance Radial Basis Function Neural Network Model
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