Decoupling Program Using Membership Function Neural Networks Integrated with Fuzzy Control

In this article, the authors introduce a decoupling program utilizing the integration of membership function neural networks with fuzzy control. This program combines two distinct technologies to improve system performance and stability. The membership function neural network is an artificial neural network designed to learn and adapt to various datasets through activation functions that mimic fuzzy membership curves. Its primary purpose is prediction and classification, making it valuable for understanding complex input-output relationships in decoupling applications through algorithms like backpropagation with customized membership-based activation functions. On the other hand, fuzzy control employs fuzzy logic principles to handle imprecise and uncertain information, often implemented using rule-based systems with IF-THEN clauses and defuzzification methods like centroid calculation. By merging these approaches, the system gains enhanced intelligence and adaptability, leading to improved performance across diverse environments. For instance, the neural network component can be coded to dynamically adjust membership functions using gradient descent, while the fuzzy controller utilizes a rule base stored in a matrix format for real-time inference. This integrated decoupling program represents a promising technology with broad applicability in various fields such as industrial automation and intelligent systems.