Fuzzy BP Neural Network Integrated Decoupling Algorithm

Based on user requirements, I will expand the text while preserving core concepts. This MATLAB-formatted source code, originally written in Chinese, implements a fuzzy BP neural network integrated decoupling algorithm and applies it to control optimization model problems. The implementation likely includes neural network initialization, backpropagation training routines, fuzzy membership functions, and decoupling control logic. The primary objective of this algorithm is to solve control optimization model problems using an integrated fuzzy BP neural network decoupling approach. By combining fuzzy BP neural networks with control optimization models, the system enhances control system optimization, improving both performance and stability. The code probably contains layered neural network architecture with input-hidden-output layers, fuzzy inference systems for handling uncertainties, and decoupling modules to separate input-output variable interactions. The decoupling algorithm within the fuzzy BP neural network effectively separates input and output variables, reducing mutual interference between variables and significantly improving control effectiveness. Key implementation aspects may include gradient descent optimization for weight updates, fuzzy rule bases for decision-making, and sensitivity analysis for decoupling validation. In summary, this MATLAB source code is designed to address control optimization model problems by employing a fuzzy BP neural network integrated decoupling algorithm. The implementation applies this approach to control system optimization, enhancing system performance and stability through sophisticated neural network training and fuzzy logic integration.