Open AccessImproving the application of fuzzy RBF neural network in temperature control system
Author(s) -
Dingfang,
Zhouyang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032054
Subject(s) - overshoot (microwave communication) , air conditioning , control theory (sociology) , artificial neural network , pid controller , computer science , fuzzy control system , temperature control , control system , control engineering , engineering , fuzzy logic , control (management) , artificial intelligence , mechanical engineering , telecommunications , electrical engineering
The temperature control system of aircraft ground air conditioning has the characteristics of complex work, non-linearity and time-varying working, the traditional PID control overshoot is too large and the response speed is low, which can not meet the needs of high efficiency and speed. In order to improve the control performance of aircraft ground air conditioning, a fuzzy RBF neural network optimized by the improved bacterial foraging(BFO) and flower pollination(PFA) algorithm was designed, and the air conditioning system was modeled and simulated. The analysis results show that the improved algorithm has better control ability than other algorithms in terms of control accutacy and anti-interference ability, effectively restraining the large lag and other problems, which provides a theoretical reference for the optimization of air conditioning temperature control system.