Wavelet Neural Network Implementation

I have developed a MATLAB program specifically for wavelet neural networks that includes both single-dimensional and multi-dimensional network architectures, making it a comprehensive solution. The implementation incorporates an advanced initialization algorithm that improves network convergence and training efficiency. Users can easily adapt the program to their specific needs by simply replacing the training dataset. The code structure is organized with clear separation between network initialization, training algorithms, and prediction modules. Key functions include wavelet basis function implementation, gradient calculation for backpropagation, and adaptive learning rate mechanisms. The program handles both regression and classification tasks through configurable output layers. For further enhancement, the program could be extended with additional features such as different optimization algorithms (e.g., Adam, RMSprop), regularization techniques, and cross-validation modules to broaden its application scope. More detailed documentation with usage examples and parameter tuning guidelines could also be provided to help users better understand and utilize this tool. Overall, this implementation serves as a practical and efficient tool that can significantly contribute to wavelet neural network research and practical applications, particularly in signal processing, pattern recognition, and time-series forecasting domains.