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Region stability analysis for switched discrete-time recurrent neural network with multiple equilibria

Neurocomputing 2017年 249卷 182-190页

作者： Gang Bao Zhigang Zeng Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control China Three Gorges University Yichang 443002 China School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China

This paper investigates a kind of switched discrete-time neural network. Such neural network is composed of multiple sub-networks and switched different sub-networks according to the states of neural network. There is no common equilibrium for all of sub-networks, i.e., multiple equilibria coexist. Firstly, a bounded condition is presented for the switched discrete-time neural network. And then sufficient conditions are derived to ensure region stability of the equilibrium points of such neural network by mathematical analysis and nonsingular M-matrix theory. Four examples are presented to verify the validity of our results.

关键词： Switched neural network Region stability Multiple equilibria

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Predicting essential proteins based on gene expression data, subcellular localization and PPI data 1

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Lei, Xiujuan Wang, Siguo Pan, Linqiang School of Computer Science Shaanxi Normal University Xi’anShaanxi710119 China Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

Predicting essential proteins is indispensable for understanding the minimal requirements of cellular survival and development. In recent years, many methods combined with the topological features of PPI networks have been proposed. However, most of these approaches ignored the intrinsic characteristics of biological attributes. This paper integrates Gene expression data, Subcellular localization and PPI networks to identify essential proteins, named GSP. We use local average connectivity and edge clustering coefficient unite with gene expression data to measure centralities of nodes. Compared with non-essential proteins, essential proteins appear more frequently in some subcellular localizations such as Nucleus and considering that different compartments play different roles, thus we integrate subcellular localization information to identify essential proteins. The computational experiment results on the yeast PPI networks show that the proposed method GSP outperforms other state-of-art methods including DC, EC, IC, SC, NC, LAC, PeC, WDC and UDoNC. © Springer Nature Singapore Pte Ltd 2017.

关键词： Proteins

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Reconstruction of compressed sensing images based on Lpnorm and modified quasi-Newton method

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第15期55卷 482-488页

作者： Gong, Ping Li, Guohua Li, Jian School of Information and Electronic Engineering Wuzhou Universtity Wuzhou Guangxi543002 China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligence Information System Wuzhou Universtity Wuzhou Guangxi543002 China

The Lpnorm which combines the advantages of L1and L0norms (0pnorm and its first-order derivative is computed. A quasi-Newton method is generated using the globally converging modified CBFGS formula and the inexact Wolfe linear search strategy. Experiments are performed under different sampling rates and image features. Experimental results show that the visual effects of images reconstructed using the proposed algorithm are better than existing methods and that the proposed algorithm achieves performance gain in terms of PSNR and SSIM.

关键词： image reconstruction

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Complete stability of delayed recurrent neural networks with Gaussian activation functions

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Neural networks : the official journal of the International Neural Network Society 2017年 85卷 21-32页

作者： Peng Liu Zhigang Zeng Jun Wang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: liupengjz@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.

This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3 equilibrium points with 0≤k≤n, among which 2 and 3-2 equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results.

关键词： Complete stability Gaussian functions Recurrent neural networks Time-varying delays

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Second-Order controllability of Multi-Agent Systems with Multiple Leaders

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Communications in Theoretical Physics 2016年第5期65卷 585-592页

作者：刘波史运涛苏厚胜韩潇 College of Science North China University of Technology Key Laboratory of Beijing for Field-Bus Technology & Automation North China University of Technology School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaHuazhong University of Science and Technology

This paper proposes a new second-order continuous-time multi-agent model and analyzes the controllability of second-order multi-agent system with multiple leaders based on the asymmetric *** paper considers the more general case:velocity coupling topology is different from location coupling *** sufficient and necessary conditions are presented for the controllability of the system with multiple *** addition,the paper studies the controllability of the system with velocity damping *** results are given to illustrate the correctness of theoretical results.

关键词： controllability second-order multi-agent systems multiple leaders asymmetric topology

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Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning

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Physical Review E 2017年第2期96卷 022411-022411页

作者： Duxin Chen Bowen Xu Tao Zhu Tao Zhou Hai-Tao Zhang Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan 430074 P.R. China Web Sciences Center University of Electronic Science and Technology of China Chengdu 610054 P.R. China

Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing challenge. Based on analysis of high spatial-temporal resolution GPS data of three pigeon flocks, we extract the hidden interaction principle by using a newly emerging machine learning method, namely the sparse Bayesian learning. It is observed that the interaction probability has an inflection point at pairwise distance of 3–4 m closer than the average maximum interindividual distance, after which it decays strictly with rising pairwise metric distances. Significantly, the density of spatial neighbor distribution is strongly anisotropic, with an evident lack of interactions along individual velocity. Thus, it is found that in small-sized bird flocks, individuals reciprocally cooperate with a variational number of neighbors in metric space and tend to interact with closer time-varying neighbors, rather than interacting with a fixed number of topological ones. Finally, extensive numerical investigation is conducted to verify both the revealed interaction and decision-making principle during circular flights of pigeon flocks.

关键词： Collective behavior Self-organization Birds Flocks Organisms

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A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm

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Neurocomputing 2017年 221卷 24-31页

作者： Rui Hu Shiping Wen Zhigang Zeng Tingwen Huang School of Automation Huazhong University of Science and Technology Wuhan PR China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan PR China Texas A & M University at Qatar Doha 5825 Qatar

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. Generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ . Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error.

关键词： Short term power load forecasting Generalized regression neural network Fruit fly optimization algorithm Decreasing step size

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Second-Order Consensus of Multi-agent Systems via Periodically Intermittent Pinning control

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Circuits, Systems, and Signal processing 2016年第7期35卷 2413-2431页

作者： Wang, Xiaoling Su, Housheng Wang, Xiaofan Liu, Bo Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China College of Science North China University of Technology Beijing100144 China

In this paper, we investigate the leader-following consensus of second-order multi-agent systems with nonlinear dynamics and time delay by employing periodically intermittent pinning control. All member agents and the virtual leader share the same nonlinear dynamics relating to the velocity and delayed velocity information. Based on Lyapunov stability theory, we obtain a novel criterion that is independent of the time delay and control width to guarantee the second-order consensus of multi-agent systems with time delay and periodically intermittent controllers. Numerical simulations are presented to illustrate the theoretical results. © 2015, Springer Science+Business Media New York.

关键词： Time delay

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Research on Adaptation Criteria Generation based on Large Data Mining 7

Research on Adaptation Criteria Generation based on Large Da...

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2016 IEEE Chinese Guidance, Navigation and control Conference (IEEE CGNCC2016)

作者： Yun Hong-quan XU Li DOU Hao MING De-lie National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute State Key Laboratory for Multi-spectral Information Processing Technologies School of AutomationHuazhong University of Science and Technology

ISBN: (纸本)9781467383196

This paper performs research on adaptation analysis based on large amount of multi-source remote sensing *** view of different demands from different task background,the research is firstly focused on how to analyze the data in computer *** achieve this,the feature parameters of target areas are extracted from different target area geographic *** combination of ORACLE database engine,data mining technology is used to carry out the target area adaptation assessment,and extract corresponding adaptation *** test the trained adaptation criteria on multi-source geographic information data of different target *** results show that the resulting criterion has certain coincidence rate and robustness.

关键词： image Research on Adaptation Criteria Generation based on Large Data Mining

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Fast KNN search for big data with set compression tree and best bin first 2

Fast KNN search for big data with set compression tree and b...

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2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

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