基于回声状态网络的开关磁阻电机建模  

Modeling of Switched Reluctance Motor Based on Echo State Network

作　　者：司利云[1] 王铁勇[2] 

机构地区：[1]长安大学电子与控制工程学院,陕西西安710064 [2]中航工业西安航空计算技术研究所,陕西西安710068

出　　处：《电机与控制应用》2015年第6期21-26,共6页Electric machines & control application

基　　金：中央高校基本科研业务费专项资金项目(CHD2011JC131);陕西省微特电机及驱动技术重点实验室开放基金项目(2013SSJ2003)

摘　　要：针对传统神经网络在开关磁阻电机建模过程中存在的网络结构确定困难和训练过程过于复杂的问题,提出了基于回声状态网络的电机建模方法。回声状态网络利用储备池和线性回归算法简化了网络设计和训练过程,使得模型具有良好的收敛速度。无需电机的任何先验知识,利用训练样本,便可建立正确反映电机磁特性的电感模型。在保证良好预测精度的前提下,与BP和RBF神经网络相比,所建模型具有计算简单,收敛速度快等优势,可进一步应用于电机的实时控制中。Focusing on the problems that the network structure is difficult to determining and the training process is too complex in the modeling process of switched reluctance motor using the traditional neural network, a motor modeling method based on echo state network was proposed. The echo state network simplified the network design and the training process using the reserve pond and the linear regression algorithm, enables the model to have the good convergence rate. Without any prior knowledge of the motor, inductance model would be established accurately to reflect the motor magnetic characteristics using training data. Under the premise to ensure good prediction accuracy, compared with BP and RBF neural network, the model had the advantages of simple calculation, fast convergence speed, and could be further applied to the real-time control of the motor.

关 键 词：开关磁阻电机 电感模型 递归神经网络 回声状态网络 收敛速度 

分 类 号：TM352[电气工程—电机]