BP Neural Network Control System Identification

This document presents the original M-file source code implementing BP neural network control system identification. After offline training, this program enables simulation of sampling point variation curves. Specifically, the system can model complex nonlinear relationships through learning from input and output data, providing accurate prediction results. The implementation includes key algorithmic components such as forward propagation for signal processing, backward error propagation for weight adjustment, and gradient descent optimization for minimizing prediction errors. This method finds extensive applications in modern industry for better understanding and controlling complex production processes. Through further improvements and optimization, this technique can also play significant roles in other domains such as finance, healthcare, and environmental monitoring, where the neural network's architecture (typically featuring input, hidden, and output layers) can be adapted for specific pattern recognition and predictive modeling tasks.