Research on Intelligent Control Learning Algorithm in Electrical Engineering Automation Based on Fuzzy Neural Network

The research of intelligent control and optimization algorithm in the field of electrical engineering and automation is one of the important directions of modern science and technology development. This paper discusses the application of intelligent control technology in electrical engineering and automation system, and analyzes the function of optimization algorithm in improving system performance, reducing cost and enhancing stability in detail. Two methods are proposed to improve the quality of regulation: when establishing rules, the intelligent heuristic function of reinforcement learning is used to search fuzzy control rules and improve the quality of generated rules. When the useless rules are deleted, the stability of the system is strengthened by gradually reducing the width of the membership function. Finally, the effectiveness of the algorithm is proved by simulation. The neural network can learn and adapt to the unknown or uncertain system, and the fuzzy control has the fuzzy reasoning ability like human brain. The organic combination of the two makes the algorithm self-learning, robust and easy to deal with nonlinearity. Through the combination of advanced control theory and algorithm, the research realizes the efficient control of electrical system and improves the level of automation. In addition, the challenges and prospects of intelligent control and optimization algorithms in solving practical problems of electrical engineering are discussed, which provides theoretical support and practical guidance for the further development of electrical engineering and automation.