원문정보
초록
영어
The core issues of RBF network design are to design the minimum structure neural networks that can meet the accuracy requirements, in order to ensure the generalization ability of the network. For the purpose of simplifying the structure of RBF network, proposes a learning method of RBF network based on improved particle swarm. The method automatically constructs frugal structure of RBF network model by the combining algorithm of regularized least squares method and D- optimal experimental design; chooses three learning parameters of the combining algorithm that can affect network generalization performance by the improved particle swarm optimization algorithm. By nonlinear time series modeling, verifies the effectiveness of the method in this paper.
목차
1. Introduction
2. Combined with the Regularized Orthogonal Least Squares Method and D- Optimal Experiment Design Method
3. IPSO
4. The Improved PSO RBF Method
4.1. The Basic Idea of RBF Network Design Method
4.2. Improved RBF Neural Network PSO Optimal Learning Method Based on Adaptive Selection Function
5. Experimental Examples
6. Conclusion
References