Open AccessDistributed filtering stochastic parameter learning algorithm for discrete-time random nonlinear systems with delay
Author(s) -
L. Francis Raj,
D. Dorathy Prema Kavitha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/2/022006
Subject(s) - filter (signal processing) , mathematics , nonlinear system , discrete time and continuous time , control theory (sociology) , covariance , noise (video) , state space , algorithm , nonlinear filter , computer science , filter design , statistics , artificial intelligence , physics , control (management) , quantum mechanics , image (mathematics) , computer vision
Distributed filtering algorithm for a discrete time random nonlinear stochastic systems associated with state delay for the distributed wireless communication sensors are discussed in this paper. Stochastic Parameter Learning Algorithm (SPLA) is tend to aim at obtaining a collection of stochastic filter parameters in a finite limited time horizon, that minimize the traces of the upper limits which is permitted to reduce the error variance matrices of the concerned stochastic filter system’s states and delay measurements. Filter gain values of the filter derived by the determination of Riccati type difference equations, estimates systems states with delay. Two different filter rules are taken into account for the SPLA discrete time random nonlinear systems with steady state space equations model. Zero mean distinct covariance matrix along with the constructive state values and constant time delay are focused in compatible dimensions. The variance of the projected systems predicted noise and the actual estimated noise are validated through numerical examples.