State Estimation for Measurement-Saturated Memristive Neural Networks with Missing Measurements and Mixed Time Delays Subject to Cyber-Attacks: A Non-Fragile Set-Membership Filtering Framework
Source abstract
Abstract
This paper is concerned with the state estimation problem based on non-fragile set-membership filtering for a class of measurement-saturated memristive neural networks (MNNs) with unknown but bounded (UBB) noises, mixed time delays and missing measurements (MMs), subject to cyber-attacks under the framework of weighted try-once-discard protocol (WTOD protocol). Considering bandwidth-limited open networks, this paper proposes an improved set-membership filtering based on WTOD protocol to partially solve the problem that multiple sensor-related problems and multiple network-induced phenomena influence the state estimation performance of MNNs. Moreover, this paper also discusses the gain perturbations of the estimator and proposes an improved non-fragile estimation framework based on set-membership filtering, which enhances the robustness of the estimation approach. The proposed estimation framework can effectively estimate the state of MNNs with UBB noises, estimator gain perturbations, mixed time-delays, cyber-attacks, measurement saturations and MMs. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. The effectiveness of the theoretical results is verified by comparative numerical simulation examples.
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Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks Stability and Synchronization · Distributed Control Multi-Agent Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Qingdao Institute of Product Quality Supervision and Inspection, Qingdao 266061, China
Funding
National Natural Science Foundation of China · Shandong Province Modern Agricultural Technology System · key project of Shandong Provincial Natural Science Foundation · National Key Research and Development Program of China · Science & Technology Specific Projects in Agricultural High-Tech Industrial Demonstration Area of the YellowRiver Delta · Shandong Provincial Key R & D Plan
References (64)
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