Constructing Neural Networks Using Wavelet Functions

This program implements source code for constructing neural networks using wavelet functions. It can be widely applied to analyze various biomedical signals, including electrocardiogram (ECG) and electroencephalogram (EEG) signals. The implementation leverages wavelet transforms for signal decomposition and feature extraction, combined with neural network architectures for pattern recognition and classification. This program serves as a powerful tool that enables researchers to deeply investigate and understand characteristics and patterns in different types of signals. Through wavelet-based preprocessing and neural network processing, researchers can effectively identify key information within signals and extract valuable data insights. The implementation includes user-friendly interfaces and multiple functionalities, making it accessible for both academic research and clinical diagnostics. The code structure supports modular design, allowing customization of wavelet types, network parameters, and signal processing pipelines. Whether in academic research or medical diagnostic applications, this program provides comprehensive analytical tools and reliable results for signal analysis tasks.