Wavelet Neural Network MATLAB Implementation

This MATLAB implementation of wavelet neural network serves as a comprehensive and adaptable program suitable for processing diverse data types including images, audio signals, and time series data. The program integrates wavelet transformation techniques with neural network algorithms, implementing key computational approaches such as multi-resolution analysis through wavelet decomposition and adaptive learning via backpropagation. Users can perform critical tasks including data denoising through thresholding of wavelet coefficients, feature extraction using wavelet packet decomposition, and pattern recognition via neural network classification. The implementation provides adjustable parameters for wavelet basis functions (e.g., Daubechies, Haar), network architecture configurations, and training parameters, enabling customized operations based on specific requirements. The codebase includes well-documented functions for wavelet transform computation, neural network initialization, and training algorithms, accompanied by comprehensive documentation and practical example codes demonstrating applications in signal processing and pattern recognition. Whether you are a beginner exploring wavelet-neural network integration or an experienced researcher, this implementation facilitates efficient research and application through its modular structure and optimization techniques for enhanced computational performance.