MATLAB Implementation of Wavelet Neural Networks

Wavelet Neural Network (WNN) is a hybrid technique that integrates wavelet analysis with neural networks, combining the advantages of wavelet analysis and the capabilities of neural networks to achieve program simplicity, understandability, and high efficiency. Wavelet analysis serves as a mathematical tool for analyzing and processing signals and data, while neural networks are computational models simulating the human brain's nervous system, possessing learning and adaptive capabilities. By integrating these two approaches, WNNs can better handle complex data and signals, delivering more accurate and reliable results. In MATLAB implementation, key functions include wavelet transformation functions (e.g., wavedec for multi-level decomposition) and neural network toolbox functions (e.g., newff for creating feedforward networks). The algorithm typically involves wavelet preprocessing of input data before feeding it into the neural network, enhancing feature extraction and convergence performance. Therefore, Wavelet Neural Networks represent a highly promising technology with broad application prospects in signal processing, pattern recognition, and predictive modeling.