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Bao G.,China Three Gorges University | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Information Processing and Intelligent Control
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - FOCI 2014: 2014 IEEE Symposium on Foundations of Computational Intelligence, Proceedings | Year: 2015

Memristor is a nonlinear resistor with the character of memory and is proved to be suitable for simulating synapse of neuron. This paper introduces two memristors in series with the same polarity (back-to-back) as simulator for neuron's synapse and presents the model of recurrent neural networks with such back-to-back memristors. By analysis techniques and fixed point theory, some sufficient conditions are obtained for recurrent neural network having single attractor flow and multiple attractors flow. At last, simulation with numeric examples is presented to illustrate our results. © 2014 IEEE. Source


Chen J.,Key Laboratory of Image Information Processing and Intelligent Control | Zeng Z.,Huazhong University of Science and Technology | Jiang P.,Key Laboratory of Image Information Processing and Intelligent Control
Information Sciences | Year: 2014

In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valued analysis, differential inclusions theory and a new Lyapunov function method, we prove that the neural network has a unique periodic solution, which is globally exponentially stable. Moreover, we prove the existence, uniqueness and global exponential stability of equilibrium point for time-varying delayed memristor-based neural networks with constant coefficients. The obtained results improve and extend previous works on memristor-based or usual neural network dynamical systems with continuous or discontinuous right-hand side. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results. © 2014 Elsevier Inc. All rights reserved. Source


Chen J.,Huazhong University of Science and Technology | Chen J.,Key Laboratory of Image Information Processing and Intelligent Control | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Information Processing and Intelligent Control | And 3 more authors.
Chinese Control Conference, CCC | Year: 2014

This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Network (GA-BPNN). A case study of Yuhuangge landslide in the Three Gorges reservoir in China is used to illustrate the capability and merit of our schemes. In addition, the result shows that GP-BPNN get better accuracy than GA-RBFN in the same measurements. © 2014 TCCT, CAA. Source


Han G.-S.,Huazhong University of Science and Technology | Han G.-S.,Key Laboratory of Image Information Processing and Intelligent Control | Guan Z.-H.,Huazhong University of Science and Technology | Guan Z.-H.,Key Laboratory of Image Information Processing and Intelligent Control | And 3 more authors.
Systems and Control Letters | Year: 2015

A multi-consensus problem is studied in multi-agent networks. The interaction mechanism of competition/abstention/cooperation among agents is introduced. Three rectangular impulsive protocols are proposed to solve multi-consensus of second order multi-agent networks with a directed topology. These algorithms have the performance of Dirac impulsive control and discrete-time control. Necessary and sufficient conditions are obtained for the stationary multi-consensus and the dynamic multi-consensus. Numerical examples are provided to illustrate the effectiveness of the obtained criteria. © 2014 Elsevier B.V. All rights reserved. Source


Lian C.,Huazhong University of Science and Technology | Lian C.,Key Laboratory of Image Information Processing and Intelligent Control | Zeng Z.,Huazhong University of Science and Technology | Zeng Z.,Key Laboratory of Image Information Processing and Intelligent Control | And 2 more authors.
Stochastic Environmental Research and Risk Assessment | Year: 2014

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. © 2014, Springer-Verlag Berlin Heidelberg. Source

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