Finite-horizon H_∞ state estimation for time-varying complex networks based on the outputs of partial nodes

Abstract

In this paper, the partial-nodes-based resilient filtering problem for a class of discrete time-varying complex networks is investigated. In order to reduce the effect of imprecision of filter parameters on estimation performance, a set of resilient filters is proposed. The measurement output from all network nodes may not be available in the actual system, but only from a fraction of nodes. The state estimators are designed for the time-varying complex network based on partial nodes to make the estimation error achieve the H ∞ performance constraint over a finite horizon. By employing the completing-the-square technique and the backward recursive Riccati difference equations, the sufficient conditions for the existence of the estimator are derived. Then the gain of the estimator is calculated. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

“…In future, we aim to 1) propose other advanced response strategies to the environmental changes; 2) seek potential integration of some state-of-the-art DMOAs for further performance enhancement; 3) develop a learnable DMOP optimizer that can realize adaptive parameter configuration; 4) employ other advanced population-based heuristic algorithms as the static optimizer [29], [47], [63], which can improve the efficiency of obtaining the Pareto solutions; 5) investigate more comprehensive quantification manners of environmental changes so as to provide in-depth and thorough insights on dynamic behaviors, which is also beneficial to the research on other dynamic systems [19], [65]; 6) improve and apply our algorithm to some real-world optimization problems such as the complex system modeling [30] and the influence maximization of complex networks [41] so as to validate and enhance the engineering practicality of the proposed HRS-DMOA.…”

Auxiliary Particle Filtering Over Sensor Networks Under Protocols of Amplify-and-Forward and Decode-and-Forward Relays

“…As one of the fundamental issues in disciplines of control and signal processing, the filtering problem has been receiving an ever-growing research interest for a few decades, see e.g. [3], [8], [13], [16], [22], [25], [44], [52] for some recent references. Among various types of filters, the particle filter (PF) stands out as a representative in the Bayesian framework that has been extensively investigated [9], [50].…”