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Multi-State-Dependent parameter model identification and estimation.

Tych, Wlodek and Sadeghi, J. and Smith, Paul and Chotai, Arun and Taylor, C. J. (2012) Multi-State-Dependent parameter model identification and estimation. In: System Identification, Environmental Modelling and Control System Design. Springer, London, pp. 191-210. ISBN 978-0-85729-973-4

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Abstract

This chapter describes the generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems, to now include Multi-State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi-path trajectory, employing the Kalman Filter and Fixed Interval Smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for their use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of non-linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are considered.

Item Type: Contribution in Book/Report/Proceedings
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Departments: Faculty of Science and Technology > Lancaster Environment Centre
Faculty of Science and Technology > Engineering
ID Code: 40761
Deposited By: Mr Richard Ingham
Deposited On: 03 May 2011 14:03
Refereed?: No
Published?: Published
Last Modified: 09 Apr 2014 20:43
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/40761

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