Source Code for Genetic Algorithm Optimized Wavelet Neural Network

The source code for genetic algorithm optimized wavelet neural network consists of the following components: 1. Constructed nonlinear function: Implemented in the nninit_test.m file. This function generates and defines the nonlinear test function used for approximation experiments, serving as the benchmark for evaluating network performance. 2. Direct WNN approximation: The Wnn_test.m file implements direct wavelet neural network (WNN) approach for nonlinear function approximation. This module internally calls wavelet basis functions to construct the network architecture, demonstrating the basic WNN implementation without optimization. 3. Genetic algorithm optimized approximation: The GA_Wnn_test.m file implements the genetic algorithm-optimized wavelet neural network (GA-WNN) for enhanced nonlinear function approximation. This comprehensive module integrates genetic algorithm components including population initialization routines, fitness evaluation functions that assess network performance, and decoding operations that convert genetic representations back into network parameters. The above provides detailed specifications for the source code implementing genetic algorithm optimized wavelet neural networks.