关键词: 深度学习, 深度置信网络, 函数拟合, 逆变器, 异步电动机控制

Abstract:

The hardware controllers were designed by means of the LTI control theory, while neural network controllers are created by approximately fitting the strategies of the hardware controllers through pre-training the neural network algorithms. The approach is based on the fact that neural network algorithms can represent nonlinear functions as exactly as desired, whenever the neural networks have sufficient complexity in terms of neurons and hidden layers numbers. To illustrate the theoretical features, the inverter switching table of an induction motor control system being replaced with deep belief network (DBN) algorithm was examined. The input/output data of the inverter switching table were used as the training reference and target data for training the DBN algorithm, which in turn replaced the inverter switching table. This yielded an induction motor system driven by the DBN-algorithm control strategies. Numerical simulations and comparisons in between time responses under the inverter switching table of hardware circuits and those of the suggested DBN ones validate the expected control performances and accuracy.

Key words: deep learning, deep belief networks (DBN), function fitting, inverter, induction motor control