PID Neural Model Based on Wavelet Neural Network Identification

Source code implementation of a PID neural model based on wavelet neural network identification. The wavelet neural network combines wavelet transform with neural networks, providing excellent feature extraction and pattern recognition capabilities. The PID neural model integrates PID control algorithms with neural networks to achieve precise system modeling and control. This implementation features wavelet transform for input signal feature extraction, followed by neural network processing for classification and pattern recognition, ultimately applying PID control algorithms for system regulation. The code structure includes wavelet decomposition layers for multi-resolution analysis, neural network hidden layers with activation functions, and PID controller modules with proportional, integral, and derivative components. Key functions handle signal preprocessing, feature dimension reduction, and adaptive PID parameter tuning based on neural network outputs. This source code is applicable to various domains requiring system identification and control using PID neural models, including industrial automation, robotics, and process control systems.