Research and Analysis of Electric Control Systems Based on Fuzzy Logic and Neural Network Integration

With the rapid development of electrical engineering, electric control systems have entered a period of high-quality development. By focusing on fuzzy logic-based neural networks, new proposals should be submitted to meet the needs of intelligent and adaptive electric control systems. Based on the dynamic evolution of electrical control systems, we establish a theoretical analysis framework for modeling electric control systems according to fuzzy logic. It allows us to explain the integrated system state identification mechanism and performance optimization of electric control systems, including fuzzy logic and neural networks. In addition, we use numerical examples and practical deduction to explore the possibility of moving toward the high-quality development of electric control systems. The development of an electric control system is to provide electrical equipment with functions that meet expected standards. Managers are committed to continuously improving electric control systems' quality and equipment satisfaction. To this end, we have taken measures, including strengthening fuzzy logic control based on fuzzy rules and internal circulation, constructing a mechanism for interaction and learning between fuzzy logic and neural network quality perception, and establishing an evaluation system for electric control system and electrical equipment, to achieve high-quality development of electric control system, boost technological upgrading, and truly meet user needs.