Key Laboratory of Image Processing and Intelligent Control of Education

Wuhan, China

Key Laboratory of Image Processing and Intelligent Control of Education

Wuhan, China
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Lian C.,Huazhong University of Science and Technology | Lian C.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Based on time series analysis, total accumulative displacement of landslide is divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes of landslide displacement and inducing factors. In this paper, a novel neural network technique called the ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Trend component displacement and periodic component displacement are forecasted respectively, then total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. A case study of Baishuihe landslide in the Three Gorges reservoir area is presented to illustrate the capability and merit of our model. © 2012 Springer-Verlag.


Sheng Y.,Huazhong University of Science and Technology | Sheng Y.,Key Laboratory of Image Processing and Intelligent Control of Education | Shen Y.,Huazhong University of Science and Technology | Shen Y.,Key Laboratory of Image Processing and Intelligent Control of Education
Journal of the Franklin Institute | Year: 2016

In this paper, we reconsider the problem of reachable set bounding for linear time-delay systems with disturbances. Generalized reachable sets for dynamical systems are proposed where initial points are taken into account. Meanwhile, a novel lemma is given which shows an extended criterion that the reachable sets are bounded. Then, based on this lemma and the Lyapunov-Krasovskii functional (LKF) as well as the free-weighting matrix techniques, improved delay-dependent linear matrix inequality (LMI) criteria are obtained for finding an ellipsoid to bound the reachable sets of linear time-delay systems with disturbances. Two numerical examples are finally provided to substantiate the efficiency and merits of our theoretical results. © 2016 The Franklin Institute.


Zhang G.,Huazhong University of Science and Technology | Zhang G.,Key Laboratory of Image Processing and Intelligent Control of Education | Shen Y.,Huazhong University of Science and Technology | Shen Y.,Key Laboratory of Image Processing and Intelligent Control of Education | And 2 more authors.
Neural Networks | Year: 2013

This paper is concerned with the global exponential anti-synchronization of a class of chaotic memristive neural networks with time-varying delays. The dynamic analysis here employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. And by using differential inclusions theory, the Lyapunov functional method and the inequality technique, some new sufficient conditions ensuring exponential anti-synchronization of two chaotic delayed memristive neural networks are derived. The new proposed results here are very easy to verify and they also improve the earlier publications. Finally, a numerical example is given to illustrate the effectiveness of the new scheme. © 2013 Elsevier Ltd.


Chen H.,Huazhong University of Science and Technology | Chen H.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education | Tang H.,Hubei Engineering University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people's life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction. © 2012 Springer-Verlag.


Wen S.,Huazhong University of Science and Technology | Wen S.,Key Laboratory of Image Processing and Intelligent Control of Education | Wen S.,Texas A&M University at Qatar | Zeng Z.,Huazhong University of Science and Technology | And 3 more authors.
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2013

This Letter is concerned with the problem of fuzzy modeling and synchronization of memristor-based Lorenz circuits with memristor-based Chua's circuits. In this Letter, a memristor-based Lorenz circuit is set up, and illustrated by phase portraits and Lyapunov exponents. Furthermore, a new fuzzy model of memristor-based Lorenz circuit is presented to simulate and synchronize with the memristor-based Chua's circuit. Through this new fuzzy model, two main advantages can be obtained as: (1) only two linear subsystems are needed; (2) fuzzy synchronization of these two different chaotic circuits with different numbers of nonlinear terms can be achieved with only two sets of gain K. Finally, numerical simulations are used to illustrate the effectiveness of these obtained results. © 2013 Elsevier B.V. All rights reserved.


Wu A.,Huazhong University of Science and Technology | Wu A.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education
IEEE Transactions on Neural Networks and Learning Systems | Year: 2012

In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks. In addition, a simplified and effective algorithm is considered for design of the optimal controller. These conditions are the improvement and extension of the existing results in the literature. Two numerical examples are given to illustrate the theoretical results via computer simulations. © 2012 IEEE.


Wen S.,Huazhong University of Science and Technology | Wen S.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education | Huang T.,Texas A&M University at Qatar
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2012

In this Letter, a memristor-based Chua's system is presented, and the chaotic behavior of this system is demonstrated by phase portraits. This Letter also deals with the problem of adaptive synchronization control of this chaotic system using the drive-response concept, and presents an adaptive control scheme for the synchronization of memristor-based Chua's circuit, when the parameters of the drive system are fully unknown and different with those of the response system. The sufficient condition for the adaptive synchronization has been analyzed. Moreover, the controller design method is further extended to more general cases, where the physical plant contains parameter uncertainties, represented in either polytopic or structured frameworks. Numerical simulations are used to demonstrate these results. © 2012 Elsevier B.V.


Zhu C.,Hubei Normal University | Bao G.,Key Laboratory of Image Processing and Intelligent Control of Education
2011 International Conference on Information Science and Technology, ICIST 2011 | Year: 2011

The global asymptotic stability of fuzzy cellular neural networks with unbounded time-varying delays and Lipschitz continuous activation functions is investigated in this brief. Based on the concept of comparison, some novel sufficient conditions for the globally asymptotic stablity of equilibria are given. © 2011 IEEE.


Wen S.,Huazhong University of Science and Technology | Wen S.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education | Huang T.,Texas A&M University at Qatar
Neural Processing Letters | Year: 2013

A memrsitor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. In this paper, we present a class of memristor-based neural circuits comprising leaky integrate-and-fire (I & F) neurons and memristor-based learning synapses. Employing these neuron circuits and corresponding SPICE models, the properties of a two neurons network are shown to be similar to biology. During correlated spiking of the pre- and post-synaptic neurons, the strength of the synaptic connection increases. Conversely, it is diminished when the spiking is uncorrelated. This synaptic plasticity and associative learning is essential for performing useful computation and adaptation in large scale artificial neural networks. Finally, future circuit design and consideration are discussed with the memristor-based neural networks. © 2012 Springer Science+Business Media New York.


Wen S.,Huazhong University of Science and Technology | Wen S.,Key Laboratory of Image Processing and Intelligent Control of Education | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Processing and Intelligent Control of Education | Huang T.,Texas A&M University at Qatar
Signal Processing | Year: 2014

This paper investigates the reliable H∞ filtering problem for a class of neutral systems with mixed delays and multiplicative noises. The mixed delays comprise both discrete and distributed delays. And the multiplicative disturbances are in the form of a scalar Gaussian white noise with unit variance. Furthermore, the failures of sensors are quantified by a variable varying in a given interval. In the presence of mixed delays and multiplicative noises, sufficient conditions for the existence of a reliable H∞ filter are derived, such that the filtering error dynamics is asymptotically mean-square stable and also achieve a guaranteed H ∞ performance level. Then a linear matrix inequality (LMI) approach for designing such a reliable H∞ filter is presented. Finally, a numerical example is provided to illustrate the effectiveness of the developed theoretical results. © 2013 Elsevier B.V.

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