具有非线性模板的CNN动态学习算法。2连续时间情况下

1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96) Pub Date : 1996-06-24 DOI:10.1109/CNNA.1996.566619

F. Puffer, R. Tetzlaff, D. Wolf

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引用次数: 4

摘要

提出了一种基于梯度的非线性模板连续时间CNN动态学习算法。应用该方法求解以偏微分方程(PDE)为特征的多维非线性系统动力学模型的CNN参数。该算法的效率与我们之前开发的非梯度学习过程的效率进行了比较。详细讨论了用非线性klein - gordon方程确定动力学的两个系统的建模结果。

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A learning algorithm for the dynamics of CNN with nonlinear templates. II. Continuous-time case

A gradient-based learning algorithm for the dynamics of continuous-time CNN with nonlinear templates is presented. It is applied in order to find the parameters of CNN that model the dynamics of certain multidimensional nonlinear systems, which are characterized by partial differential equations (PDE). The efficiency of the algorithm is compared to that of a non-gradient-based learning procedure we have previously developed. Results for modeling two systems, whose dynamics are determined by nonlinear Klein-Gordon-equations, are discussed in detail.

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1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96)

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