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Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

Yajing Yu, Hongli Dong, Zidong Wang, Weijian Ren, Fuad E. Alsaadi
2016 Neurocomputing  
This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays.  ...  The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties.  ...  The simulation results verify the effectiveness of the developed algorithm for designing the non-fragile state estimator for discrete-time neural networks with parameter uncertainties.  ... 
doi:10.1016/j.neucom.2015.11.079 fatcat:bs7bxsz4hjao7k2l4t25svv3fm

Non-fragile state estimation for discrete Markovian jumping neural networks

Nan Hou, Hongli Dong, Zidong Wang, Weijian Ren, Fuad E. Alsaadi
2016 Neurocomputing  
In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays.  ...  In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks.  ...  In this paper, we deal with the non-fragile state estimation problem for a class of discrete-time neural networks with Markovian jumping parameters and time-delays.  ... 
doi:10.1016/j.neucom.2015.11.089 fatcat:svdlpbspunc4zjwvspyrfadkhm

A new approach to non-fragile state estimation for continuous neural networks with time-delays

Fan Yang, Hongli Dong, Zidong Wang, Weijian Ren, Fuad E. Alsaadi
2016 Neurocomputing  
In this paper, the non-fragile state estimation problem is investigated for a class of continuous neural networks with time-delays and nonlinear perturbations.  ...  The main purpose of the addressed problem is to design a non-fragile state estimator for the recurrent delayed neural networks such that the dynamics of the estimation error converges to the equilibrium  ...  In particular, a state estimator has been designed in [27] for discrete-time neural networks with Markovian jumping parameters and time-varying delays.  ... 
doi:10.1016/j.neucom.2016.02.062 fatcat:ffyq4rhgnnbo5mkd5us4kiys34

Page 8412 of Mathematical Reviews Vol. , Issue 2004j [page]

2004 Mathematical Reviews  
Summary: “This paper considers the problems of robust non- fragile control for uncertain discrete-delay large-scale systems un- der state feedback gain variations.  ...  A key step in this procedure is the derivation of a polytopic boundary for the state-space matrices of the IOF linearized system based on the es- timated parameters of the neural network and their uncertainty  ... 

2009 Index IEEE Transactions on Automatic Control Vol. 54

2009 IEEE Transactions on Automatic Control  
., +, TAC Oct. 2009 2477 -2480 Non-Fragile Exponential Stability Assignment of Discrete-Time Linear Systems With Missing Data in Actuators.  ...  ., +, TAC Sept. 2009 2100-2113 Non-Fragile Exponential Stability Assignment of Discrete-Time Linear Systems With Missing Data in Actuators.  ...  State Convergence of Passive Nonlinear Systems With an L Input.  ... 
doi:10.1109/tac.2009.2037798 fatcat:4ilhkzss6jc63ersjzi47hiwgu

Extended dissipativity and non-fragile synchronization for Recurrent neural networks with multiple time-varying delays via sampled-data control

R. Anbuvithya, S. Dheepika Sri, R. Vadivel, Nallappan Gunasekaran, P. Hammachukiattikul
2021 IEEE Access  
A non-fragile sampled-data approach is applied to investigate the problem of neural networks with multiple time-varying delays, which ensures that the master system synchronizes with the slave system and  ...  Vadivel and Porpattama Hammachukiattikul was supported by the Phuket ABSTRACT This paper deals with the extended dissipativity and non-fragile synchronization of delayed recurrent neural networks (RNNs  ...  Finally, non-fragile technique along with the sampled-data controller has been used to synchronize delayed neural networks.  ... 
doi:10.1109/access.2021.3060044 fatcat:b4tvzdvctjbn7gjg5yt62ptepa

Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

Qiang Li, Jinling Liang
2022 Applied Mathematics and Computation  
This article is devoted to the non-fragile asynchronous state estimation problem for Markovian switching complex-valued networks subject to randomly occurring nonlinearities (RONs) and external stochastic  ...  In addition, the estimator gains desired can be appropriately designed by resorting to feasible solutions of a set of complex matrix inequalities.  ...  By utilizing the similar method employed in this paper, non-fragile state-dependent controller could also be designed when considering the filtering problem for neural network with discrete time form in  ... 
doi:10.1016/j.amc.2021.126583 fatcat:jxvrrbrbtzcttetube3yymcfxy

Finite-Time Nonfragile Dissipative Filter Design for Wireless Networked Systems with Sensor Failures

R. Sakthivel, V. Nithya, Yong-Ki Ma, Chao Wang
2018 Complexity  
In this study, the problem of finite-time nonfragile dissipative-based filter design for wireless sensor networks that is described by discrete-time systems with time-varying delay is investigated.  ...  By constructing a suitable Lyapunov-Krasovskii functional and employing discrete-time Jensen's inequality, a new set of sufficient conditions is established in terms of linear matrix inequalities such  ...  Acknowledgments The work of Yong-Ki Ma was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No.2015R1C1A1A01054663)  ... 
doi:10.1155/2018/7482015 fatcat:iugh7tvy5vdvdi7zrlwkw4jlqq

A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

Zhibin Hu, Jun Hu, Guang Yang
2020 Systems Science & Control Engineering  
In this paper, some recent results on the distributed filtering, estimation and fusion algorithms for nonlinear systems with communication constraints are reviewed.  ...  Finally, conclusion is given and several possible research topics on distributed filtering, estimation and fusion for nonlinear networked systems are pointed out. ARTICLE HISTORY  ...  For example, in Ding et al. (2012) , the problem of the distributed H ∞ state estimation has been examined for a class of discrete timevarying nonlinear systems with stochastic parameters over sensor  ... 
doi:10.1080/21642583.2020.1737846 fatcat:e7nmy2fzkbekvg43pmd63kwqk4

Variance-constrained resilient H ∞ $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Yan Gao, Jun Hu, Dongyan Chen, Junhua Du
2019 Advances in Difference Equations  
This paper addresses the resilient H ∞ state estimation problem under variance constraint for discrete uncertain time-varying recurrent neural networks with randomly varying nonlinearities and missing  ...  Our main purpose is to design a time-varying state estimator such that, for all missing measurements, randomly varying nonlinearities and estimator gain perturbation, both the estimation error variance  ...  For example, in [27, 28] , the H ∞ state estimation design strategies have been proposed for discrete delayed neural networks, where the impacts from the multiple missing measurements have been discussed  ... 
doi:10.1186/s13662-019-2298-7 fatcat:wiismq4urvfb7nkmf4zbqozzke

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks [article]

Ehsan Kharazmi, Min Cai, Xiaoning Zheng, Guang Lin, George Em Karniadakis
2021 medRxiv   pre-print
For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks (NNs).  ...  ABSTRACTWe analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify multiple time-dependent parameters and to discover new data-driven  ...  In the PINN formulation, we use separate deep neural networks with input t to represent the states U(t) and (timedependent) parameters.  ... 
doi:10.1101/2021.04.05.21254919 fatcat:4y56xqkrwzexhipugj3kh4pymm

Event-triggered stochastic synchronization in finite time for delayed semi-Markovian jump neural networks with discontinuous activations

Min Liu, Huaiqin Wu, Wei Zhao
2020 Computational and Applied Mathemathics  
In this paper, the global stochastic synchronization in finite time is discussed for discontinuous semi-Markovian switching neural networks with mixed time-varying delays under stochastic disturbance based  ...  Keywords Discontinuous neural networks • Semi-Markovian switching • Event-triggered non-fragile control scheme • Stochastic synchronization in finite time • Mixed time-varying delays • Noise disturbance  ...  Compliance with ethical standards  ... 
doi:10.1007/s40314-020-01146-2 fatcat:l24grfl7ozekdeu5hasnryf54e

Disturbance-observer-based robust control for time delay uncertain systems

Mou Chen, Wen-Hua Chen
2010 International Journal of Control, Automation and Systems  
The guaranteed cost control was investigated for parameter uncertain systems with time delay in [1] . Kim [2] studied the robust stability of time-delayed linear systems with uncertainties.  ...  A robust control scheme is proposed for a class of systems with uncertainty and time delay based on disturbance observer technique.  ...  Kim and Oh [17] proposed the robust and non-fragile H ∞ control for descriptor systems with parameter uncertainties and time delay.  ... 
doi:10.1007/s12555-010-0233-5 fatcat:vxaqmkkav5bgxh3m2ww3grwfwu

Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Yongduan Song, Jennie Si, Sonya Coleman, Dermot Kerr
2022 IEEE Transactions on Neural Networks and Learning Systems  
An adaptive robust controller is designed to diminish the influence from model nonlinearities and parameter uncertainties for accurate control.  ...  It aims to solve optimal impulsive control problems for discrete-time nonlinear systems.  ... 
doi:10.1109/tnnls.2022.3161003 fatcat:4e6v2kclcbb5pgkqqsyyaiwzjy

Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation

Xinzhe Yuan, Dustin Tanksley, Pu Jiao, Liujun Li, Genda Chen, Donald Wunsch
2021 Frontiers in Built Environment  
Contemporary methods such as convolutional neural networks (CNNs) for time series classification and seismic damage evaluation face a challenge in training due to a huge task of ground-motion image encoding  ...  a three-channel AVD image of the ground motion event with a pre-defined size of width × height.  ...  Note that the squared RP image size with a time delay embedding τ and dimension m of the state s i is n − (m − 1)τ, where n is the total number of data points in a time series.  ... 
doi:10.3389/fbuil.2021.660103 fatcat:whri6ia6l5allfsv6rd55qlboi
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