国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

A fault diagnosis model for oil-immersed transformers based on an improved particle swarm optimization (IPSO) optimized T-S adaptive neuro fuzzy inference system (T-S ANFIS) is proposed to enhance the accuracy and efficiency of fault diagnosis. The dynamic inertia weight and linear learning factor adjustment strategy are introduced to overcome the problem of premature convergence and local optima in particle swarm optimization. The convergence domain and Euclidean distance are utilized to distinguish identical particles. Furthermore, the T-S ANFIS"s premise parameters are optimized using IPSO to improve the network"s convergence speed. The effectiveness of the IPSO-optimized T-S ANFIS fault diagnosis model is verified through simulation experiments. The results demonstrate that the proposed model achieves an optimal fault diagnosis accuracy of approximately 98%, which is higher than that of ANFIS and PSO-ANFIS models, indicating its high accuracy and efficiency in fault diagnosis.