Wavelet RBF Neural Network Research Articles

Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network

Combining the good pattern recognition performance of neural network and the data processing performance of wavelet decomposition, this paper combines wavelet analysis with neural network and uses particle swarm optimization radial basis function neural network to diagnose the fault of electric vehicle battery. In this paper, for large data volume and redundant data, wavelet decomposition is used to process signal processing, including data noise reduction and feature vector extraction. In the fault diagnosis of electric vehicle battery system, a radial basis function RBF neural network based on particle swarm optimization (PSO) optimization is proposed. Finally, the reliability and feasibility of the proposed algorithm are proved by simulation.

The multidimensional function approximation based on constructive wavelet RBF neural network

For the multidimensional continuous function, using constructive feedforward wavelet RBF neural network, we prove that a wavelet RBF neural network with n + 1 hidden neurons can interpolate n + 1 multidimensional samples with zero error. Then we prove they can uniformly approximate any continuous multidimensional function with arbitrary precision. This method can avoid the defects of conventional neural networks using learning algorithm in practice. The correctness and effectiveness are verified through four numeric experiments.