Tchebycheff正交神经网络的动态建模方法研究

RESEARCH ON DYNAMIC MODELING BASED ON TCHEBYCHEFF ORTHOGONAL NEURAL NETWORK

  • 摘要: 本文提出一种正交神经网络的动态建模方法,它充分利用了Tchebycheff多项式的非线性处理能力和Givens正交变换的有效处理大型稀疏问题的优点,不仅能快速进行网络的训练,而且能对网络的结构进行优化,为非线性系统的动态建模提供了一种有效方法.实验表明它是一种简单的、普遍适用的系统建模方法.

     

    Abstract: This paper presents a dynamic modeling method based on orthogonal neural network. It fully uses the characteristics of the nonlinear processing ability of Tchebycheff polynomial and the efficient disposal of the large scaling sparse problems that givens transform can process. It can not only train the network quickly, but also optimize the structure of the network. Simulating experiments show that the new modeling method is a simple universal modeling approach for the nonlinear systems.

     

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