Linear System State Estimation Using Hopfield Net

Qinwei Sun, A. Alouani
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引用次数: 1

Abstract

The purpose of this paper is to use human knowledge in dynamic optimization theory in combination with neural network to solve dynamic optimization problems. It is shown in this paper that one can perform linear state estimation using Hopfield neural net. This is done by formulating the state estimation problem as a dynamic optimization problem. It is found that the Hopfield net is a suitable computing device for this problem. In addition, the synaptic weights and bias vectors are computed online, using optimal control theory. Case studies are presented and the performance of the neural net based state estimator is compared to the Kalman filter performance.
基于Hopfield网络的线性系统状态估计
本文的目的是利用人类在动态优化理论方面的知识,结合神经网络来解决动态优化问题。本文证明了利用Hopfield神经网络可以进行线性状态估计。这是通过将状态估计问题表述为动态优化问题来实现的。结果表明,Hopfield网络是解决这一问题的理想计算工具。此外,利用最优控制理论在线计算突触权值和偏置向量。给出了实例研究,并将基于神经网络的状态估计器的性能与卡尔曼滤波器的性能进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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