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GA based optimization of NN-SANARX model for adaptive control of nonlinear systems

机译:基于GA的非线性系统自适应控制的NN-SANARX模型优化。

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This paper discusses application of dynamic state feedback algorithm for adaptive control of nonlinear MIMO systems. Neural Network based Simplified Additive Nonlinear AutoRegressive eXogenous (NN-SANARX) structure is used for identification of nonlinear MIMO systems. For better and faster adaptation it is important to minimize the number of parameters to be tuned. Therefore, structural identification of the neural network is done by the genetic algorithm. To avoid some of the complications caused by on-line adaptation the model is divided into adaptable and nonadaptable parts.
机译:本文讨论了动态状态反馈算法在非线性MIMO系统自适应控制中的应用。基于神经网络的简化加法非线性自回归异质(NN-SANARX)结构用于识别非线性MIMO系统。为了更好更快地进行适配,重要的是最小化要调整的参数数量。因此,神经网络的结构识别是通过遗传算法完成的。为了避免在线适应引起的一些复杂性,该模型分为适应性部分和非适应性部分。

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