非线性时滞系统的多维泰勒网络辨识

Chenlong Li, Hong-sen Yan
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引用次数: 4

摘要

将多维泰勒网络(MTN)模型与改进的共轭梯度(ICG)方法相结合,提出了一种用于非线性时滞系统辨识的方法——ICG-MTN。利用MTN的强逼近特性,将其作为识别模型,将ICG方法作为MTN的学习算法。同时,将反向传播神经网络(BPNN)作为比较方法。实验结果表明,该方法可以有效地识别非线性时滞系统,且有效性优于bp神经网络。
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Identification of Nonlinear Time-delay System Using Multi-dimensional Taylor Network Model
Combining multi-dimensional Taylor network (MTN) model with improved conjugate gradient (ICG) method, named ICG-MTN, is proposed for identification of nonlinear time-delay system in this paper. MTN is regarded as the identification model relying on its characteristic of strong approximation, and ICG method is regarded as the learning algorithm of MTN. Meanwhile, back propagation neural network (BPNN) is regarded as the method of comparison. By the experimental results, the nonlinear time-delay system can be identified effectively by the proposed method, and the effectiveness is better than the BPNN.
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