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Tian E, Wu Z, Xie X. Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:6156-6165. [PMID: 37015670 DOI: 10.1109/tnnls.2022.3230056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.
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Sakthivel R, Kwon OM, Choi SG, Sakthivel R. Observer-based state estimation for discrete-time semi-Markovian jump neural networks with round-robin protocol against cyber attacks. Neural Netw 2023; 165:611-624. [PMID: 37364471 DOI: 10.1016/j.neunet.2023.05.046] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/27/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023]
Abstract
This paper investigates an observer-based state estimation issue for discrete-time semi-Markovian jump neural networks with Round-Robin protocol and cyber attacks. In order to avoid the network congestion and save the communication resources, the Round-Robin protocol is used to schedule the data transmissions over the networks. Specifically, the cyber attacks are modeled as a set of random variables satisfying the Bernoulli distribution. On the basis of the Lyapunov functional and the discrete Wirtinger-based inequality technique, some sufficient conditions are established to guarantee the dissipativity performance and mean square exponential stability of the argument system. In order to compute the estimator gain parameters, a linear matrix inequality approach is utilized. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed state estimation algorithm.
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Affiliation(s)
- Ramalingam Sakthivel
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Oh-Min Kwon
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea.
| | - Seong-Gon Choi
- School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Rathinasamy Sakthivel
- Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India; Department of Mathematics, Sungkyunkwan University, Suwon 440746, South Korea.
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Wan P, Wang X, Pang G, Wang L, Min G. A novel rumor detection with multi-objective loss functions in online social networks. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119239. [PMID: 36407849 PMCID: PMC9650513 DOI: 10.1016/j.eswa.2022.119239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.
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Affiliation(s)
- Pengfei Wan
- School of Computer Science, Shaanxi Normal University, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
| | - Xiaoming Wang
- School of Computer Science, Shaanxi Normal University, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
| | - Guangyao Pang
- School of Computer Science, Shaanxi Normal University, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
| | - Liang Wang
- School of Computer Science, Shaanxi Normal University, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, West Chang'an Avenue, Xi'an, 710119, Shaanxi province, China
| | - Geyong Min
- Department of Computer Science, University of Exeter, The Queen's Drive, Exeter, EX44QF, Devonshire, United Kingdom
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Mohammadian M, Sufi Karimi H. Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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5
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Narayanan G, Ali MS, Alsulami H, Saeed T, Ahmad B. Synchronization of T–S Fuzzy Fractional-Order Discrete-Time Complex-Valued Molecular Models of mRNA and Protein in Regulatory Mechanisms with Leakage Effects. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11010-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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6
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Li B, Wang Z, Han QL, Liu H. Distributed Quasiconsensus Control for Stochastic Multiagent Systems Under Round-Robin Protocol and Uniform Quantization. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:6721-6732. [PMID: 33079691 DOI: 10.1109/tcyb.2020.3026001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, the problem of consensus control is investigated for a class of multiagent systems (MASs) with both stochastic noises and nonidentical exogenous disturbances. The signal transmission among agents is implemented through a digital communication network subject to both uniform quantization and round-robin protocol as a reflection of network constraints. The consensus strategy is designed by adopting the estimates of the relative states of the agent to its neighbors, which renders the distributed nature of the controller. A new consensus concept, namely, quasiconsensus in probability, is employed to evaluate the state response of the agents to the stochastic noises, the exogenous disturbances, and the quantization error. An augmented system is first formed that relies on the deviations of the individual state from the average state, the observer error of the relative state, as well as the relative measurement output. Based on the augmented model, an analysis approach on dynamical behaviors is developed to facilitate the consensus analysis of MASs by means of the switching Lyapunov function technique and the stochastic analysis methods. Then, the existence condition and the explicit expression of the time-varying gain matrices are proposed for the expected controller by resorting to the feasibility of several matrix inequalities. Numerical simulation results are presented to demonstrate the applicability of the theoretical results.
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Wang J, Wang H, Shen H, Wang B, Park JH. Finite-Time H ∞ State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1651-1660. [PMID: 33242311 DOI: 10.1109/tcbb.2020.3040979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article is concerned with the problem of finite-time H∞ state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is considered in this paper. Besides, several random variables that obey the Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this article is to design an estimator to ensure that the estimation error system is stochastically finite-time bounded and satisfies the H∞ performance. Based on this, sufficient conditions for the explicit form of the estimator gains can be obtained by the Lyapunov method. Finally, a numerical example is given to verify the correctness and feasibility of the proposed method.
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Li J, Dong H, Liu H, Han F. Sampled-data non-fragile state estimation for delayed genetic regulatory networks under stochastically switching sampling periods. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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State estimator design for genetic regulatory networks with leakage and discrete heterogeneous delays: A nonlinear model transformation approach. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.03.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Song Q, Chen Y, Zhao Z, Liu Y, Alsaadi FE. Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.08.059] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Wang L, He H, Zeng Z, Hu C. Global Stabilization of Fuzzy Memristor-Based Reaction-Diffusion Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:4658-4669. [PMID: 31725407 DOI: 10.1109/tcyb.2019.2949468] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article investigates the global stabilization problem of Takagi-Sugeno fuzzy memristor-based neural networks with reaction-diffusion terms and distributed time-varying delays. By using the Green formula and proposing fuzzy feedback controllers, several algebraic criteria dependent on the diffusion coefficients are established to guarantee the global exponential stability of the addressed networks. Moreover, a simpler stability criterion is obtained by designing an adaptive fuzzy controller. The results derived in this article are generalized and include some existing ones as special cases. Finally, the validity of the theoretical results is verified by two examples.
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Li Q, Wang Z, Li N, Sheng W. A Dynamic Event-Triggered Approach to Recursive Filtering for Complex Networks With Switching Topologies Subject to Random Sensor Failures. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:4381-4388. [PMID: 31831444 DOI: 10.1109/tnnls.2019.2951948] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article deals with the recursive filtering issue for a class of nonlinear complex networks (CNs) with switching topologies, random sensor failures and dynamic event-triggered mechanisms. A Markov chain is utilized to characterize the switching behavior of the network topology. The phenomenon of sensor failures occurs in a random way governed by a set of stochastic variables obeying certain probability distributions. In order to save communication cost, a dynamic event-triggered transmission protocol is introduced into the transmission channel from the sensors to the recursive filters. The objective of the addressed problem is to design a set of dynamic event-triggered filters for the underlying CN with a certain guaranteed upper bound (on the filtering error covariance) that is then locally minimized. By employing the induction method, an upper bound is first obtained on the filtering error covariance and subsequently minimized by properly designing the filter parameters. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.
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13
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Qu Y, Pang K. State estimation for a class of artificial neural networks subject to mixed attacks: A set-membership method. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Song Q, Long L, Zhao Z, Liu Y, Alsaadi FE. Stability criteria of quaternion-valued neutral-type delayed neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.086] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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15
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Liu H, Wang Z, Fei W, Li J. H ∞ and l 2-l ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol. Neural Netw 2020; 132:121-130. [PMID: 32871337 DOI: 10.1016/j.neunet.2020.08.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/19/2020] [Accepted: 08/10/2020] [Indexed: 11/26/2022]
Abstract
In this paper, a protocol-based finite-horizon H∞ and l2-l∞ estimation approach is put forward to solve the state estimation problem for discrete-time memristive neural networks (MNNs) subject to time-varying delays and energy-bounded disturbances. The Round-Robin protocol is utilized to mitigate unnecessary network congestion occurring in the sensor-to-estimator communication channel. For the delayed MNNs, our aim is to devise an estimator that not only ensures a prescribed disturbance attenuation level over a finite time-horizon, but also keeps the peak value of the estimation error within a given range. By resorting to the Lyapunov-Krasovskii functional method, the delay-dependent criteria are formulated that guarantee the existence of the desired estimator. Subsequently, the estimator gains are obtained via figuring out a bank of convex optimization problems. The validity of our estimator is finally shown via a numerical example.
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Affiliation(s)
- Hongjian Liu
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China.
| | - Zidong Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Weiyin Fei
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; School of Mathematics and Physics, Anhui Polytechnic University, Wuhu 241000, China.
| | - Jiahui Li
- Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
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Ding D, Wang Z, Han QL. A Scalable Algorithm for Event-Triggered State Estimation With Unknown Parameters and Switching Topologies Over Sensor Networks. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:4087-4097. [PMID: 31199280 DOI: 10.1109/tcyb.2019.2917543] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
An event-triggered distributed state estimation problem is investigated for a class of discrete-time nonlinear stochastic systems with unknown parameters over sensor networks (SNs) subject to switched topologies. An event-triggered communication strategy is employed to govern the information broadcast and reduce the unnecessary resource consumption. Based on the adopted communication strategy, a distributed state estimator is designed to estimate the plant states and also identify the unknown parameters. In the framework of input-to-state stability, sufficient conditions with an average dwell time are established to ensure the boundedness of estimation errors in mean-square sense. In addition, the gains of the designed estimators are dependent on the solution of a set of matrix inequalities whose dimensions are unrelated to the scale of underlying SNs, thereby fulfill the scalability requirement. Finally, an illustrative simulation is utilized to verify the feasibility of the proposed design scheme.
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17
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Ding D, Wang Z, Han QL. Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3719-3730. [PMID: 31329155 DOI: 10.1109/tcyb.2019.2927471] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.
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Guo J, Wang Z, Zou L, Zhao Z. Ultimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocol. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20154134. [PMID: 32722359 PMCID: PMC7435392 DOI: 10.3390/s20154134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 07/16/2020] [Accepted: 07/21/2020] [Indexed: 06/11/2023]
Abstract
This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy.
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Affiliation(s)
- Jiyue Guo
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; (J.G.); (Z.Z.)
| | - Zidong Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; (J.G.); (Z.Z.)
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, Middlesex, UK;
| | - Lei Zou
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, Middlesex, UK;
| | - Zhongyi Zhao
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; (J.G.); (Z.Z.)
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Hou N, Wang Z, Ho DWC, Dong H. Robust Partial-Nodes-Based State Estimation for Complex Networks Under Deception Attacks. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:2793-2802. [PMID: 31217136 DOI: 10.1109/tcyb.2019.2918760] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, the partial-nodes-based state estimators (PNBSEs) are designed for a class of uncertain complex networks subject to finite-distributed delays, stochastic disturbances, as well as randomly occurring deception attacks (RODAs). In consideration of the likely unavailability of the output signals in harsh environments from certain network nodes, only partial measurements are utilized to accomplish the state estimation task for the addressed complex network with norm-bounded uncertainties in both the network parameters and the inner couplings. The RODAs are taken into account to reflect the compromised data transmissions in cyber security. We aim to derive the gain parameters of the estimators such that the overall estimation error dynamics satisfies the specified security constraint in the simultaneous presence of stochastic disturbances and deception signals. Through intensive stochastic analysis, sufficient conditions are obtained to guarantee the desired security performance for the PNBSEs, based on which the estimator gains are acquired by solving certain matrix inequalities with nonlinear constraints. A simulation study is carried out to testify the security performance of the presented state estimation method.
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Shen Y, Wang Z, Shen B, Alsaadi FE, Dobaie AM. l 2-l ∞ state estimation for delayed artificial neural networks under high-rate communication channels with Round-Robin protocol. Neural Netw 2020; 124:170-179. [PMID: 32007717 DOI: 10.1016/j.neunet.2020.01.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/27/2019] [Accepted: 01/14/2020] [Indexed: 11/16/2022]
Abstract
In this paper, the l2-l∞ state estimation problem is addressed for a class of delayed artificial neural networks under high-rate communication channels with Round-Robin (RR) protocol. To estimate the state of the artificial neural networks, numerous sensors are deployed to measure the artificial neural networks. The sensors communicate with the remote state estimator through a shared high-rate communication channel. In the high-rate communication channel, the RR protocol is utilized to schedule the transmission sequence of the numerous sensors. The aim of this paper is to design an estimator such that, under the high-rate communication channel and the RR protocol, the exponential stability of the estimation error dynamics as well as the l2-l∞ performance constraint are ensured. First, sufficient conditions are given which guarantee the existence of the desired l2-l∞ state estimator. Then, the estimator gains are obtained by solving two sets of matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the developed l2-l∞ state estimation scheme.
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Affiliation(s)
- Yuxuan Shen
- College of Information Science and Technology, Donghua University, Shanghai 200051, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Bo Shen
- College of Information Science and Technology, Donghua University, Shanghai 200051, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Abdullah M Dobaie
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Li Q, Wang Z, Sheng W, Alsaadi FE, Alsaadi FE. Dynamic event-triggered mechanism for H∞ non-fragile state estimation of complex networks under randomly occurring sensor saturations. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.08.063] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Meng F, Li K, Zhao Z, Song Q, Liu Y, Alsaadi FE. Periodicity of impulsive Cohen–Grossberg-type fuzzy neural networks with hybrid delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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