基于神经网络的开关磁阻电动机建模

针对常规的BP算法存在收敛速度慢,容易陷入局部最小值的缺点,提出了一种提高BP网络学习速度的方法,并基于BP神经网络建立了开关磁阻电动机磁特性Ψ(θ,i)模型.神经网络的参数是经过优化选择的,训练的时间和步数都大为减少,程序运行稳定,经过训练、识别、预测三重整定,具有很强的学习泛化能力,大大增强了系统的实时性和鲁棒性.该模型有助于进一步优化能量转换,减小转矩脉动.

Model of Switched Reluctance Motor Based on Neural Networks

Aimed at the disadvantages of the general BP arithmetic, such as convergence speed slowly and easily plunged in the minimum of the local, this article puts forward a method of raising the study speed of BP network and build up a model of magnetic characterisitics Ψ(θ, i) of SRM based on BP neural network . The parameters of neural networks in this article has been optimizedly selected, the training time and step number has greatly been reduced. The procedure runs smoothly, after being trained, recogrized and forecasted three fixes, and it has strong capability of studying generalization, strengthened greatly the real-time and robustness of the system. The magnetic characterisitics model built in this article is critical to the optimized energy conversion and the reduced torque ripple.