Wavelet Neural Network (WNN) Implementation with Genetic Algorithm Optimization

The original wavelet neural network source code consists of the following components: 1. Nonlinear Function Construction: Implemented in nninit_test.m file. This module creates custom nonlinear test functions used for training and validating the wavelet neural network, providing the benchmark data for approximation tasks. 2. Direct WNN Nonlinear Approximation: Located in Wnn_test.m. This implementation directly applies wavelet neural networks to approximate nonlinear functions, internally calling wavelet basis functions for feature transformation and network activation. 3. Genetic Algorithm Optimized Approximation: Contained in GA_Wnn_test.m. This module integrates genetic algorithm optimization with WNN, incorporating specialized functions for population initialization, fitness evaluation, and chromosome decoding to enhance the network's performance through evolutionary computation. Additionally, the genetic algorithm optimized WNN source code includes: 1. Nonlinear Function Building: nninit_test.m - Provides the foundation for generating complex nonlinear patterns that challenge the network's approximation capabilities. 2. Direct WNN Approach: Wnn_test.m - Demonstrates the core wavelet neural network implementation using wavelet transforms as activation functions for improved feature extraction and approximation accuracy. 3. Genetic Algorithm Enhanced WNN: GA_Wnn_test.m - Combines evolutionary computation techniques with wavelet neural networks, featuring robust genetic operators for initialization, fitness calculation, and solution decoding to achieve superior optimization results.