混沌神经网络在DTC系统中的转速辨识

Rotor Speed Identification on DTC System Based on Chaos Neural Network

针对BP神经网络易陷入局部极小值的缺点,提出了混沌神经网络的学习算法。利用混沌的遍历性和随机性,采用混沌变量全局粗搜索与混沌变量局部细搜索相结合,得到神经网络权值的全局最优值。利用该算法对直接转矩控制(DTC)系统进行转速辨识,仿真结果表明用混沌优化BP神经网络的速度辨识器不仅具有较好的跟踪能力,还提高了运算效率,使系统具有良好的静动态性能。

To solve the disadvantage that BP neural network is liable to get into the local minimum, a novel learning algorithm that chaos neural network is proposed. By the use of the properties of ergodicity and randomness of chaos, and combining global rough search and local elaborate search of chaos variable, get the global optimization weight values of neural network. Using it in speed identification of direct torque control （DTC） system, the simulation results show that the rotor speed identification using the BP neural network optimized by chaos optimization algorithm not only has better track capability, but also enhance the operation effectiveness, it makes the system have favorable static and dynamic preperties.