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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. Source


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. Source


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. Source


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. Source


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. Source

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