The problem of interest concerns the estimation of an unobserved discrete-time random signal in a dynamic system.
This paper is concerned with the mixed H∞ and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set Γ="{0,1} is introduced to model this phenomenon.
There is a class of estimation problems requiring optimal filtering at a discrete-time current point n employing measurement on an averaging interval (horizon) of preceding or/and succeeding neighboring but not obligatorily nearest N points.
Typically, training symbols are inserted in the data burst for estimation of the equivalent discrete-time channel model.
But, to the authors best knowledge, the proposed parameter estimation methods are not applied for estimation of the equivalent discrete-time channel model.
A discrete-time model for the p53-Mdm2 feedback system was also built for parameter estimation, which will be described in later sections of the paper.
FSM is a discrete-time, discrete-state dynamical system which has a finite number of states.
This paper discusses the estimation of a class of discrete-time linear stochastic systems with statistically-constrained unknown inputs (UI), which can represent an arbitrary combination of a class of un-modeled dynamics, random UI with unknown covariance matrix and deterministic UI.
