A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Foundations of Wavelet Networks and Applications
Foundations of Wavelet Networks and Applications
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
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This paper presents a wavelet neural-network for learning and approximation of non-linear functions. Wavelet networks are a class of neural network that takes advantage of good localization and approximation properties of multiresolution analysis. The proposed model structure is similar to that of Radial Basis Function (RBF) structure and we have restricted to non-orthogonal continuous wavelets i.e. the first and second derivative of Gaussians as wavelet functions. Simulations show that the proposed method outperforms the other reported methods.