Iterative parameter identification methods for nonlinear functions
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TL;DR: Two gradient based iterative algorithms and a Newton iterative algorithm are presented to determine the parameters of a nonlinear system by using the negative gradient search method and Newton method in order to enhance computational efficiencies.
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About: This article is published in Applied Mathematical Modelling. The article was published on 01 Jun 2012. and is currently open access. The article focuses on the topics: Local convergence & Iterative method.
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Citations
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Three-stage recursive least squares parameter estimation for controlled autoregressive autoregressive systems
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Gradient based iterative algorithms for solving a class of matrix equations
Feng Ding,Tongwen Chen +1 more
TL;DR: A hierarchical identification principle is applied to study solving the Sylvester and Lyapunov matrix equations, and it is proved that the iterative solution consistently converges to the true solution for any initial value.
491
Iterative least-squares solutions of coupled Sylvester matrix equations
Feng Ding,Tongwen Chen +1 more
TL;DR: A general family of iterative methods to solve linear equations, which includes the well-known Jacobi and Gauss–Seidel iterations as its special cases, are presented and it is proved that the iterative solution consistently converges to the exact solution for any initial value.
429
Identification of Hammerstein nonlinear ARMAX systems
Feng Ding,Tongwen Chen +1 more
TL;DR: Two identification algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates.
412
Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle
Feng Ding,Peter X. Liu,Jie Ding +2 more
TL;DR: It is proved that the iterative solution always converges to the exact solution for any initial values.
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