文献类型：期刊文献

中文题名：基于UKF和神经网络的一类非线性系统状态估计

英文题名：State estimation of a class of nonlinear system based on UKF and neural network

作者：刘济[1];高丽君[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2014

卷号：29

期号：11

起止页码：2076

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20145000326082);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：上海市自然科学基金项目(11ZR1409800)

语种：中文

中文关键词：模型未知;神经网络;不敏卡尔曼滤波

外文关键词：unknown model; neural network; unscented Kalman filter

摘要：在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.
It is significant to estimate the important process variables when process models are unknown.Therefore,the method of combining unscented Kalman filter（UKF） with neural network is used to solve the state estimation problems for a class of nonlinear systems whose models are unknown.The dynamic neural network is used to model for the nonlinear system,and the state and weights are updated at the same time by using UKF,which can achieve the purposes that the neural network approximate the real model,and the estimated values follow the real values.Two simulation examples are given to verify that the proposed approach gets good effects of estimation,and the greater the proportion of state in the output,the higher the estimation precision.