非线性系统辨识:预测误差法与神经网络

Jinming Sun, Yanqiu Huang, Wanli Yu, A. Ortiz
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摘要

系统辨识已广泛应用于各个领域,用于分析系统特性,进行过滤、预测和自动控制。预测误差法(PEM)是估计系统参数和开发系统动态结构的经典方法之一;而神经网络(NN)则对结构未知的黑箱系统较为有利。随着物联网(IoT)和网络物理系统(CPS)的普及,识别任务越来越多地转向资源受限的设备。因此,一些研究将系统先验知识引入到神经网络中以提高其效率。然而,目前尚不清楚是否适应的神经网络优于经典的PEM。本文在几种常见非线性系统的估计精度和速度方面对两种方法进行了比较。结果表明,神经网络具有更广泛的适用性和准确性,但从计算量的角度来看,代价较高;而PEM更轻量,但在系统输入频繁突变时存在局限性。
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Nonlinear System Identification: Prediction Error Method vs Neural Network
System identification has been used in various domains for analyzing system properties and carrying out filtering, prediction and automatic control. Prediction error method (PEM) is one of the classic methods to estimate system parameters and exploit dynamical structure of the studied system; while neural network (NN) is favorable for black-box systems with unknown structures. As the popularity of Internet of Things (IoT) and Cyber-physical systems (CPS) increases, the identification tasks are moving more towards resource-constrained devices. Accordingly, some studies incorporate system prior knowledge into NN to improve its efficiency. However, it is unclear whether the adapted NN outperforms the classic PEM.This paper provides a fair comparison between two techniques in terms of estimation accuracy and speed on several common nonlinear systems. The results indicate that NN is wider applicable and accurate, but more expensive from computational perspective; whereas PEM is more lightweight, but has limitations when the system input has frequent abrupt changes.
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