Open AccessFault Diagnosis of HVAC System Considering LMBP Neural Network Method
Author(s) -
Guan Xiao-man
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 - 10.1088/1742-6596/1533/3/032013
Subject(s) - artificial neural network , overshoot (microwave communication) , hvac , computer science , pid controller , control theory (sociology) , matlab , backpropagation , algorithm , fault (geology) , convergence (economics) , maxima and minima , control system , control engineering , engineering , artificial intelligence , control (management) , temperature control , mathematics , air conditioning , mechanical engineering , telecommunications , mathematical analysis , economic growth , economics , operating system , electrical engineering , seismology , geology
Given the complexity of the mathematical model for the HVAC system and the constant changes of multiple external disturbances and working conditions, the application of intelligent control in the HVAC control system to improve the control performance of the HVAC control system has become a research hotspot. The Levenberg-Marquart algorithm (LM algorithm) is studied to address the defects of slow convergence speed and prone to falling into local minimum in the learning process of the BP neural network. To solve the two issues (the choice of the learning rate, and the solution of the inverse matrix) that significantly affect the training time and convergence accuracy, we improved and optimized the LM algorithm using the LU decomposition method and implemented the algorithm through the MATLAB programming language. The LMBP neural network method obtained is applied to the control loop of the HVAC chilled water cycle. The simulation comparison study is performed on its control effect and that of the PID control algorithm and BP neural network PID control algorithm. The research results show that the LMBP neural network method has significantly improved the performance in aspects such as reducing overshoot, accelerating convergence speed and reducing steady-state error, etc.