MATLAB Implementation of Wavelet Neural Networks

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Resource Overview

Wavelet Neural Network with J output nodes and I input nodes, featuring wavelet-based activation functions and network architecture.

Detailed Documentation

A Wavelet Neural Network (WNN) is a neural network model characterized by I input nodes and J output nodes. This network architecture is based on wavelet analysis theory, enabling effective analysis and processing of input data. In MATLAB implementation, the network typically uses wavelet functions as activation functions in hidden layers, replacing traditional sigmoid or tanh functions. The training process often involves gradient descent algorithms with wavelet coefficient optimization. WNNs find extensive applications in signal processing, image analysis, and pattern recognition domains. The research and development of wavelet neural networks significantly contribute to advancing artificial intelligence technologies, providing effective solutions for various practical problems. Key MATLAB functions for implementation may include wavelet transform functions (e.g., wavedec, waverec) and neural network toolbox functions for custom activation function implementation.

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