title: Optimisation on the least squares identification of dynamical systems with application to hemodynamic modelling.
abstract: Dynamic modelling using the traditional least squares method with noisy input/output data can yield biased and sometimes unstable model predictions. This is largely because the cost function employed by the traditional least squares method is based on the one-step-ahead prediction errors. In this paper, the model-predicted-output errors are used in estimating the model parameters. As the cost function is highly nonlinear in terms of the model parameters, the particle swarm optimisation method is used to search for the optimal parameters. We will show that compared with model predictions using the traditional least squares method, the model-predicted-output approach is more robust at dealing with noisy input/output data. The algorithm is applied to identify the dynamic relationship between changes in cerebral blood flow and volume due to evoked changes in neural activity and is shown to produce better predictions than that using the least squares method.
authors: Department of Psychology, Sheffield University, United Kingdom.
authors: Yi
authors: Y
authors: Pan
authors: Ying
authors: Y
authors: Zheng
authors: Sam
authors: S
authors: Harris
authors: Daniel
authors: D
authors: Coca
authors: David
authors: D
authors: Johnston
authors: John
authors: J
authors: Mayhew
authors: Stephen
authors: S
authors: Billings
mesh_terms: D000465:Algorithms; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods; D004867:Equipment Design; D006439:Hemodynamics; D006801:Humans; D016018:Least-Squares Analysis; D008955:Models, Cardiovascular; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism / Q000473:pathology; D017711:Nonlinear Dynamics; D013997:Time Factors