Wavelet Neural Network Source Code Implementation

The wavelet neural network source code consists of the following MATLAB files: 1. The file nninit_test.m handles the construction of nonlinear functions for the wavelet neural network. This module defines the mathematical structure and parameter configuration of nonlinear activation functions, typically implementing wavelet-based activation functions with adjustable scaling and translation parameters to capture multi-resolution features. 2. The file Wnn_test.m implements direct nonlinear approximation using wavelet neural networks. This script contains the core WNN architecture that calls internal wavelet functions (such as Morlet or Mexican Hat wavelets) to approximate complex nonlinear relationships through forward propagation and error backpropagation algorithms. 3. The file GA_Wnn_test.m performs optimized approximation using genetic algorithms. This advanced implementation integrates evolutionary computation with neural networks, featuring genetic operators for population initialization, fitness evaluation based on approximation error metrics, and chromosome decoding functions to convert genetic representations into viable network parameters for optimized weight and threshold configurations. Each module demonstrates different approaches to wavelet neural network implementation, ranging from basic function approximation to hybrid intelligent optimization techniques combining neural networks with evolutionary algorithms.