Parameter estimation of systems with delays via structural sensitivity analysis

This article presents a method for sensitivity analysis of non-linear continuous-time models with delays and its application to parameter estimation. The method is universal and may be used for sensitivity analysis of any system given as a block diagram with arbitrary structure and any number of delays. The method gives sensitivity functions of model trajectories with respect to all model parameters, including delay times, and both forward and adjoint sensitivity analysis may be performed. Two examples application of the method are presented: identification of a Wiener model with delay and identification of a model of JAK-STAT cell signal transduction mechanism.

[1]  Michel Fliess,et al.  Parameters estimation of systems with delayed and structured entries , 2009, Autom..

[2]  D. Cacuci Sensitivity theory for nonlinear systems. I. Nonlinear functional analysis approach , 1981 .

[3]  E. Bai,et al.  Block Oriented Nonlinear System Identification , 2010 .

[4]  Sirish L. Shah,et al.  Time delay estimation for MIMO dynamical systems – With time-frequency domain analysis , 2010 .

[5]  Yinggan Tang,et al.  Parameter estimation for time-delay chaotic system by particle swarm optimization , 2009 .

[6]  Yinggan Tang,et al.  Parameter estimation of chaotic system with time-delay: A differential evolution approach , 2009 .

[7]  José Cruz,et al.  System Sensitivity Analysis: Benchmark Papers in Electrical Engineering and Computer Science , 1975 .

[8]  J. Timmer,et al.  Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Bernt Wennberg,et al.  State elimination and identifiability of the delay parameter for nonlinear time-delay systems , 2008, Autom..

[10]  Jose B. Cruz,et al.  Feedback systems , 1971 .

[11]  Samuel J. Mason,et al.  Feedback Theory-Some Properties of Signal Flow Graphs , 1953, Proceedings of the IRE.

[12]  Krzysztof Fujarewicz,et al.  Adjoint Systems for Models of Cell Signaling Pathways and their Application to Parameter Fitting , 2007, TCBB.

[13]  Krzysztof Fujarewicz,et al.  Generalized Backpropagation through Time for Continuous Time Neural Networks and Discrete Time Measurements , 2004, ICAISC.

[14]  Krzysztof Fujarewicz,et al.  On fitting of mathematical models of cell signaling pathways using adjoint systems. , 2005, Mathematical biosciences and engineering : MBE.

[15]  Biao Huang,et al.  Improved identification of continuous-time delay processes from piecewise step tests , 2007 .

[16]  Fathalla A. Rihan Sensitivity analysis for dynamic systems with time-lags , 2003 .

[17]  K. Fujarewicz Identification and suboptimal control of heat exchanger using generalized back propagation through time , 2000 .

[18]  Kok Lay Teo,et al.  An Optimization Approach to State-Delay Identification $ $ , 2010, IEEE Transactions on Automatic Control.

[19]  A. Roy Chowdhury,et al.  Parameter estimation of a delay dynamical system using synchronization in presence of noise , 2007 .

[20]  Jean-Pierre Richard,et al.  Time-delay systems: an overview of some recent advances and open problems , 2003, Autom..