Abstract
In this paper, a novel WNN, multi-input and multi-output feedforward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feedforward network. The training formulas based on BP algorithm are mathematically derived and training algorithm is presented. A numerical experiment is given to validate the application of this wavelet neural network in multi-variable functional approximation. © Springer-Verlag 2004.
Cite
CITATION STYLE
Zhao, J., Chen, W., & Luo, J. (2004). Feedforward wavelet neural network and multi-variable functional approximation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 32–37. https://doi.org/10.1007/978-3-540-30497-5_6
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