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Suo J, Li N, Li Q. Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.01.131] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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2
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Guan X, Hu J, Qi J, Chen D, Zhang F, Yang G. Observer-based H∞ sliding mode control for networked systems subject to communication channel fading and randomly varying nonlinearities. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.01.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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3
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Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities. Neural Netw 2020; 130:143-151. [DOI: 10.1016/j.neunet.2020.06.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/04/2020] [Accepted: 06/29/2020] [Indexed: 11/20/2022]
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4
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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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Li J, Dong H, Wang Z, Bu X. Partial-Neurons-Based Passivity-Guaranteed State Estimation for Neural Networks With Randomly Occurring Time Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:3747-3753. [PMID: 31714236 DOI: 10.1109/tnnls.2019.2944552] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov-Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example.
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Liu H, Ma L, Wang Z, Liu Y, Alsaadi FE. An overview of stability analysis and state estimation for memristive neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.066] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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7
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Liu H, Wang Z, Shen B, Dong H. Delay-Distribution-Dependent H ∞ State Estimation for Discrete-Time Memristive Neural Networks With Mixed Time-Delays and Fading Measurements. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:440-451. [PMID: 30207975 DOI: 10.1109/tcyb.2018.2862914] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper addresses the H ∞ state estimation issue for a sort of memristive neural networks in the discrete-time setting under randomly occurring mixed time-delays and fading measurements. The main purpose of the addressed issue is to propose a state estimator design algorithm that ensures the error dynamics of the state estimation to be stochastically stable with a prespecified H ∞ disturbance attenuation index. We put forward certain switching functions to account for the discrete-time yet state-dependent characteristics of the memristive connection weights. By resorting to the robust analysis theory and the Lyapunov-functional analysis theory, we derive some sufficient conditions to guarantee the desired estimation performance. The derived sufficient conditions rely not only on the size of discrete time-delays and the probability distribution law of the distributed time-delays but also on the statistics information of the coefficients of the adopted Rice fading model. Based on the established existence conditions, the gain matrices of the desired estimator are obtained by means of the feasibility of a set of matrix inequalities that can be checked efficiently via available software packages. Finally, the numerical simulation results are provided to show the validity of the main results.
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Yu Y, Dong H, Wang Z, Li J. Delay-distribution-dependent non-fragile state estimation for discrete-time neural networks under event-triggered mechanism. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-3516-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Chen W, Ding D, Mao J, Liu H, Hou N. Dynamical performance analysis of communication-embedded neural networks: A survey. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.08.088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu Y, Wang Z, Zhou D. UKF-based remote state estimation for discrete artificial neural networks with communication bandwidth constraints. Neural Netw 2018; 108:393-398. [DOI: 10.1016/j.neunet.2018.08.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/27/2018] [Accepted: 08/13/2018] [Indexed: 10/28/2022]
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Huang H, Huang T, Cao Y. Reduced-Order Filtering of Delayed Static Neural Networks With Markovian Jumping Parameters. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:5606-5618. [PMID: 29994081 DOI: 10.1109/tnnls.2018.2806356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The reduced-order filtering problems are investigated in this paper for static neural networks with Markovian jumping parameters and mode-dependent time-varying delays. By fully making use of integral inequalities, the designs of reduced-order and filters are discussed. The proper gain matrices of filters and the optimal performance indices are efficiently obtained by resolving corresponding convex optimization problems with the constraints of linear matrix inequalities. It is verified that the computational complexity for the reduced-order filter design is significantly reduced when compared with the full-order one. Furthermore, the nonfragile reduced-order filtering problems are also resolved in this paper. Two examples with simulation results are presented to demonstrate the feasibility and application of the established results.
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Liu H, Wang Z, Shen B, Huang T, Alsaadi FE. Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays. Neural Netw 2018; 102:1-9. [DOI: 10.1016/j.neunet.2018.02.003] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 12/05/2017] [Accepted: 02/02/2018] [Indexed: 11/28/2022]
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13
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Ji H, Zhang H, Li C, Senping T, Lu J, Wei Y. H ∞ control for time-delay systems with randomly occurring nonlinearities subject to sensor saturations, missing measurements and channel fadings. ISA TRANSACTIONS 2018; 75:38-51. [PMID: 29486893 DOI: 10.1016/j.isatra.2018.02.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 02/01/2018] [Accepted: 02/12/2018] [Indexed: 06/08/2023]
Abstract
The H∞ control problem for a class of time-delay systems with randomly occurring nonlinearities (RONs) is addressed in this paper. Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions. The measurement output is subject to both data missing and randomly occurring sensor saturations (ROSSs) described by sector-nonlinearities as well as the channel fadings caused typically in wireless communication. The aim of the addressed problem is to design a full-order dynamic output-feedback controller such that the closed-loop system is exponentially mean-square stable and satisfies the prescribed H∞ performance constraint. Sufficient conditions are presented by resorting to intensive stochastic analysis and matrix inequality techniques, which not only guarantee the existence of the desired controller for all possible time-delays, RONs, missing measurements and ROSSs but also lead to the explicit expressions of such controllers. Finally, a numerical example is given to demonstrate the applicability of the proposed control scheme.
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Affiliation(s)
- Huihui Ji
- School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, PR China.
| | - He Zhang
- School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China.
| | - Chenlong Li
- School of Science, Tianjin University, Tianjin 300072, PR China.
| | - Tian Senping
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, PR China.
| | - Junwei Lu
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, Jiangsu 210042, PR China.
| | - Yunliang Wei
- School of Mathematical Sciences, Qufu Normal University, Qufu, Shandong 273165, PR China.
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14
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On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2980-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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