Accurate Temperature Control Method for Intelligent Moxibustion Equipment
In order to solve the problems of imprecise temperature control,long transition time,large overshoot and complex parameter setting in the process of moxibustion treatment simulated by moxibustion equipment,this paper proposes a PID parameter self-tuning algorithm based on improved fuzzy neural network.This paper explores the pa-rameter optimization of fuzzy neural networks,introduces the gradient method iteration in the improved genetic algo-rithm,synthesizes the advantages of both,and proposes a hybrid learning algorithm for fuzzy neural networks.Simula-tion and training results show that the hybrid learning algorithm proposed in this paper can complete network training well,and the advantages of the hybrid learning algorithm are verified by comparing it with the traditional BP algorithm.The simulation of the fuzzy neural network PID control system established in this paper shows that it has the characteristics of small overshoot,short transition time and rapid response,which provides a reference for moxibus-tion equipment to realize accurate temperature control of various moxibustion methods.
万方数据
维普
