The True Linearization Coefficients for Nonlinear Systems Under Parametric White Noise Excitations

2004 ◽  
Vol 74 (1) ◽  
pp. 161-163 ◽  
Author(s):  
Giovanni Falsone
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Nonlinear Response ◽  
Original System ◽  
Linearization Method ◽  
Maximum Likelihood Estimates ◽  
Stationary Case ◽  
Stochastic Linearization ◽  
Linearization Coefficients ◽  
White Noises

In this paper some properties of the stochastic linearization method applied to nonlinear systems excited by parametric Gaussian white noises are discussed. In particular, it is shown that the linearized quantities, obtained by the author in another paper by linearizing the coefficients of the Ito differential rule related to the original system, show the same properties found by Kozin with reference to nonlinear system excited by external white noises. The first property is that these coefficients are the true linearized quantities, in the sense that their exact values are able to give the first two statistical moments of the true response. The second property is that, in the stationary case and in the field of the parameter estimation theory, they represent the maximum likelihood estimates of the linear model quantities fitting the original nonlinear response.

scholarly journals On an extension of the stochastic linearization

1993 ◽  
Vol 15 (4) ◽  
pp. 1-6
Author(s):  
Di Paola Mario ◽  
Nguyen Dong Anh
Keyword(s):  
Nonlinear Systems ◽  
Nonlinear Equation ◽  
Random Excitation ◽  
Linearization Method ◽  
Mean Square ◽  
Fundamental Idea ◽  
Stochastic Linearization ◽  
The Mean ◽  
The Difference

Stochastic linearization method is one of the most useful tools for analysis of nonlinear systems under random excitation. The fundamental idea of the classical stochastic linearization consists in replacing the original nonlinear equation by a linear one in such a way that the difference between two equations is minimized in the mean square value. In this paper a new version of the stochastic linearization is proposed. It is shown that for two nonlinear systems considered the new version gives good results for both the weak and strong nonlinearities.

RKH Space Methods for Low Level Monitoring and Control of Nonlinear Systems II. A Vector-Case Example: The Lorenz System

1997 ◽  
Vol 07 (06) ◽  
pp. 823-845 ◽  
Author(s):  
Alan Cover ◽  
James Reneke ◽  
Suzanne Lenhart ◽  
Vladimir Protopopescu
Keyword(s):  
Nonlinear Systems ◽  
Reproducing Kernel ◽  
Lorenz System ◽  
Linearization Method ◽  
Monitoring And Control ◽  
Stochastic Linearization ◽  
Vector Case ◽  
The Lorenz System ◽  
And Control ◽  
Level Monitoring

By using techniques from the theory of reproducing kernel Hilbert (RKH) spaces, we continue the exploration of the stochastic linearization method for possibly unknown and/or noise corrupted nonlinear systems. The aim of this paper is twofold: (a) the stochastic linearization formalism is explicitly extended to the vector case; and (b) as an illustration, the performance of the stochastic linearization for monitoring and control is assessed in the case of the Lorenz system for which the dynamic behavior is known independently.

Exact Stationary Response Solution for Second Order Nonlinear Systems Under Parametric and External White Noise Excitations: Part II

1988 ◽  
Vol 55 (3) ◽  
pp. 702-705 ◽  
Author(s):  
Y. K. Lin ◽  
Guoqiang Cai
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Probability Distribution ◽  
White Noise ◽  
Probability Density ◽  
Detailed Balance ◽  
Stationary Probability ◽  
Probability Flow ◽  
Parametric And External Excitations ◽  
White Noises

A systematic procedure is developed to obtain the stationary probability density for the response of a nonlinear system under parametric and external excitations of Gaussian white noises. The procedure is devised by separating the circulatory portion of the probability flow from the noncirculatory flow, thus obtaining two sets of equations that must be satisfied by the probability potential. It is shown that these equations are identical to two of the conditions established previously under the assumption of detailed balance; therefore, one remaining condition for detailed balance is superfluous. Three examples are given for illustration, one of which is capable of exhibiting limit cycle and bifurcation behaviors, while another is selected to show that two different systems under two differents sets of excitations may result in the same probability distribution for their responses.

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

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