一类维纳非线性系统的递推参数估计方法研究
摘要
本文针对一类维纳非线性系统,结合递阶辨识原理,把该系统分为两个子系统,分别极小化两个准则函数,推导出了递推最小二乘辨识方法。此方法可以推广到其它维纳非线性系统的辨识,且能够用于在线辨识。仿真结果表明,此方法简单,且计算量小,可以给出高精度参数估计。
引文
[1]李秀英,韩志刚.一种基于粒子群优化的非线性系统辨识方法[J].控制与决策,2011,26(11):1627—1631.
[2]丁锋.系统辨识新论[M].北京:科学出版社,2013
[3]王双剑,楚纪正.Hammerstein模型的改进新型神经动力学辨识方法及其在混合建模中的应用[J].信息与控制,2010,4(3):386-390.
[4]陈曦,方海涛.状态空间模型下的Hammerstein系统的递推子空间辨识方法[J].自动化学报,2010,36(10):1460—1467.
[5]吴德会非线性动态系统的Wiener神经网络辨识法[J].控制理沦与应用,2009,26(11):1192-1196.
[6]F.Ding,T.Chen.Identification of Hammerstein nonlinear ARMAX systems[J].Automatica,2005,4 1(9):1479-1489.
[7]D.Q.Wang.Y.Y.Chu.G.W.Yang,F.Ding.Auxiliary modelbased recursive generalized least squar es parameter estimation for Hammerstein OEAR systems[J].Mathematical and Computer Modelling,20 10,52(1-2):309-317.
[8]J.Chen,X.P.Wang,R.F.Ding.Gradient based estimation algorithm for Hammerstein systems with saturation and dead—zone nonlinearities[J].Applied Mathematical Modelling,2012,36(l):23,S-243.
[9]D.Q.Wang,F.Ding.Least squares based and gradient based iterative identification for Wiener nonlinear systems[J],Signal Processing,2011,91(5):1182-1189.
[10]李妍,毛志忠,王琰,等.基于偏差补偿递推最小二乘的HammersteinWiener模型辨识[J].自动化学报,2010,36(1):163-168.
[11]徐小平,钱富才,王峰,等.一种辨识Wiener-Hammerstein模型的新方法[J].控制与决策,2008,23(8):929-934.
[12]M.M.Liu,Y.S,Xiao,R.F.Ding.Iterative ident ificationalgorithm for Wiener nonlinear systems using the Newton method[J]..Applied Mathematical Modelling,2013,37(9):6584-6591.
[13]F.Ding,X.P.Liu,G.Liu.Identification methods for Hammerstein nonlinear systems[J].Digital Signal Processing,2011,21(2):215—238.
[2]丁锋.系统辨识新论[M].北京:科学出版社,2013
[3]王双剑,楚纪正.Hammerstein模型的改进新型神经动力学辨识方法及其在混合建模中的应用[J].信息与控制,2010,4(3):386-390.
[4]陈曦,方海涛.状态空间模型下的Hammerstein系统的递推子空间辨识方法[J].自动化学报,2010,36(10):1460—1467.
[5]吴德会非线性动态系统的Wiener神经网络辨识法[J].控制理沦与应用,2009,26(11):1192-1196.
[6]F.Ding,T.Chen.Identification of Hammerstein nonlinear ARMAX systems[J].Automatica,2005,4 1(9):1479-1489.
[7]D.Q.Wang.Y.Y.Chu.G.W.Yang,F.Ding.Auxiliary modelbased recursive generalized least squar es parameter estimation for Hammerstein OEAR systems[J].Mathematical and Computer Modelling,20 10,52(1-2):309-317.
[8]J.Chen,X.P.Wang,R.F.Ding.Gradient based estimation algorithm for Hammerstein systems with saturation and dead—zone nonlinearities[J].Applied Mathematical Modelling,2012,36(l):23,S-243.
[9]D.Q.Wang,F.Ding.Least squares based and gradient based iterative identification for Wiener nonlinear systems[J],Signal Processing,2011,91(5):1182-1189.
[10]李妍,毛志忠,王琰,等.基于偏差补偿递推最小二乘的HammersteinWiener模型辨识[J].自动化学报,2010,36(1):163-168.
[11]徐小平,钱富才,王峰,等.一种辨识Wiener-Hammerstein模型的新方法[J].控制与决策,2008,23(8):929-934.
[12]M.M.Liu,Y.S,Xiao,R.F.Ding.Iterative ident ificationalgorithm for Wiener nonlinear systems using the Newton method[J]..Applied Mathematical Modelling,2013,37(9):6584-6591.
[13]F.Ding,X.P.Liu,G.Liu.Identification methods for Hammerstein nonlinear systems[J].Digital Signal Processing,2011,21(2):215—238.