A Novel Reference Model-Based Neural Network Approach to Temperature Control System

With the continuous development of deep learning, neural network with its excellent self-learning performance obtains a series of a major breakthrough in target detection, image recognition and so on. In this paper, the temperature control system based on a neural network combined with I-PD compensation is proposed. To improve the neural network self-learning efficiency, the reference model is introduced for providing the teaching signal of neural network. The system simulations are carried out in MATLAB/SIMULINK environment to verify the control efficiency of the proposed reference model based neural network control system. The experiments are carried out on the DSP based temperature control system platform, the results are compared to the conventional I-PD control system to verify the control efficiency.