Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks
Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks
复制标题
通过连续时间循环神经网络逼近非自治动态系统
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
K. Warwick
中科院分区：
文献类型：
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作者：
C. Kambhampati;F. Garces;K. Warwick
这项工作提供了一个通过动态循环神经网络近似 x/spl dot/=f(x)+g(x)u 形式的动态系统的框架。这扩展了先前的工作，其中证明了自主动态系统的近似实现。给定某些条件，动态 n 维神经模型的前 p 个输出神经单元以所需的接近程度逼近 p 维动态系统，其中 n>p。然后，所研究的神经架构在非线性多变量系统识别案例研究中成功实现。
This work provides a framework for the approximation of a dynamic system of the form x/spl dot/=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.