Identification and control of dynamical systems using neural networks.
Identification and control of dynamical systems using neural networks.
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DOI：
10.1109/72.80202
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发表时间：
1990-01-01
影响因子：
--
通讯作者：
Parthasarathy, K
中科院分区：
文献类型：
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作者：
Narendra, K S;Parthasarathy, K
证明神经网络可以有效地用于非线性动力学系统的识别和控制。重点是用于识别和控制的模型。讨论了调整参数的静态和动态反向传播方法。在引入的模型中，多层和经常性网络在新型配置中互连，因此实际上需要以统一的方式研究它们。仿真结果表明，所建议的识别和自适应控制方案实际上是可行的。整个过程中都介绍了基本概念和定义，还描述了必须解决的理论问题。
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described.