Aiming at the nonlinear hysteresis curve of the giant magnetostrictive material (GMM) , the generalized regression neural network (GRNN) and feed-forward BP neural network were applied to approach it. With the training and prediction of the networks, as well as comparing with the Jiles-Atherton(J-A)model, the approaching effect of the networks was analyzed, which guides the applying of the GMM well. Between them,the GRNN was trained by cross-validation method in order to enlarge the sample capacity. The best radial basis function expansion coefficient (SPREAD) was found out using circulation, and the conventional GRNN was optimized. The results indicate that the accuracy on the hysteresis curve predicted by optimized GRNN is obviously higher than the one done by BP.%针对超磁致伸缩材料(GMM)的磁滞非线性,运用广义回归神经网络(GRNN)和前馈BP神经网络分别对GMM的磁滞回线进行非线性逼近,通过网络的训练、预测,与Jiles-Atherton (J-A)模型进行了对比,分析了两种神经网络的逼近效果,给GMM的运用起到了很好的指导作用.其中,在GRNN神经网络中,由于所取数据有限,为了扩大样本容量,采取交叉验证方法对GRNN神经网络进行了训练,采用循环算法找出了最佳的径向基函数扩展系数SPREAD,并对传统GRNN神经网络进行了优化.研究结果表明:优化后的GRNN神经网络对于磁滞回线的预测精度明显高于BP神经网络.
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