Physics

Aerospace engineering

Nonlinear system

Mathematics

Algorithm

Filter (signal processing)

Mathematical optimization

Nonlinear filter

Bernoulli's principle

Engineering

Mathematical analysis

Political science

Artificial intelligence

Filter design

Set (abstract data type)

Control (management)

Programming language

Deception

Computer science

Quantum mechanics

Computer vision

Filtering problem

Control theory (sociology)

Upper and lower bounds

IEEE Transactions on Neural Networks and Learning Systems

This article tackles the recursive filtering problem for a class of stochastic nonlinear time-varying complex networks (CNs) suffering from both the state saturations and the deception attacks. The nonlinear inner coupling and the state saturations are taken into account to characterize the nonlinear nature of CNs. From the defender's perspective, the randomly occurring deception attack is governed by a set of Bernoulli binary distributed white sequence with a given probability. The objective of the addressed problem is to design a state-saturated recursive filter such that, in the simultaneous presence of the state saturations and the randomly occurring deception attacks, a certain upper bound is guaranteed on the filtering error covariance, and such an upper bound is then minimized at each time instant. By employing the induction method, an upper bound on the filtering error variance is first constructed in terms of the solutions to a set of matrix difference equations. Subsequently, the filter parameters are appropriately designed to minimize such an upper bound. Finally, a numerical simulation example is provided to demonstrate the feasibility and usefulness of the proposed filtering scheme.

State-Saturated Recursive Filter Design for Stochastic Time-Varying Nonlinear Complex Networks Under Deception Attacks