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Observers for Canonic Models of Neural Oscillators

D. Fairhurst, I. Tyukin, H. Nijmeijer, C. van Leeuwen (2010)

Mathematical Modelling of Natural Phenomena

We consider the problem of state and parameter estimation for a class of nonlinear oscillators defined as a system of coupled nonlinear ordinary differential equations. Observable variables are limited to a few components of state vector and an input signal. This class of systems describes a set of canonic models governing the dynamics of evoked potential in neural membranes, including Hodgkin-Huxley, Hindmarsh-Rose, FitzHugh-Nagumo, and Morris-Lecar...

On determining unknown functions in differential systems, with an application to biological reactors

Éric Busvelle, Jean-Paul Gauthier (2003)

ESAIM: Control, Optimisation and Calculus of Variations

In this paper, we consider general nonlinear systems with observations, containing a (single) unknown function ϕ . We study the possibility to learn about this unknown function via the observations: if it is possible to determine the [values of the] unknown function from any experiment [on the set of states visited during the experiment], and for any arbitrary input function, on any time interval, we say that the system is “identifiable”. For systems without controls, we give a more or less complete...

On determining unknown functions in differential systems, with an application to biological reactors.

Éric Busvelle, Jean-Paul Gauthier (2010)

ESAIM: Control, Optimisation and Calculus of Variations

In this paper, we consider general nonlinear systems with observations, containing a (single) unknown function φ. We study the possibility to learn about this unknown function via the observations: if it is possible to determine the [values of the] unknown function from any experiment [on the set of states visited during the experiment], and for any arbitrary input function, on any time interval, we say that the system is “identifiable”. For systems without controls, we give a more or less complete...

On-line wavelet estimation of Hammerstein system nonlinearity

Przemysław Śliwiński (2010)

International Journal of Applied Mathematics and Computer Science

A new algorithm for nonparametric wavelet estimation of Hammerstein system nonlinearity is proposed. The algorithm works in the on-line regime (viz., past measurements are not available) and offers a convenient uniform routine for nonlinearity estimation at an arbitrary point and at any moment of the identification process. The pointwise convergence of the estimate to locally bounded nonlinearities and the rate of this convergence are both established.

Optimization and identification of nonlinear uncertain systems

Jong Yeoul Park, Yong Han Kang, Il Hyo Jung (2003)

Czechoslovak Mathematical Journal

In this paper we consider the optimal control of both operators and parameters for uncertain systems. For the optimal control and identification problem, we show existence of an optimal solution and present necessary conditions of optimality.

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