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arXiv 2018年

作者： Liu, Hui Xu, Xuanhong Lu, Jun-An Chen, Guanrong Zeng, Zhigang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China School of Mathematics and Statistics Wuhan University Wuhan430072 China Department of Electronic Engineering City University of Hong Kong Hong Kong Hong Kong

Pinning control of a complex network aims at forcing the states of all nodes to track an external signal by controlling a small number of nodes in the network. In this paper, an algebraic graph-theoretic condition is introduced to optimize pinning control. When individual node dynamics and coupling strength of the network are given, the effectiveness of pinning scheme can be measured by the smallest eigenvalue of the grounded Laplacian matrix obtained by deleting the rows and columns corresponding to the pinned nodes from the Laplacian matrix of the network. The larger this smallest eigenvalue, the more effective the pinning scheme. Spectral properties of the smallest eigenvalue are analyzed using the network topology information, including the spectrum of the network Laplacian matrix, the minimal degree of uncontrolled nodes, the number of edges between the controlled node set and the uncontrolled node set, etc. The identified properties are shown effective for optimizing the pinning control strategy, as demonstrated by illustrative examples. Finally, for both scale-free and small-world networks, in order to maximize their corresponding smallest eigenvalues, it is better to pin the nodes with large degrees when the percentage of pinned nodes is relatively small, while it is better to pin nodes with small degrees when the percentage is relatively large. This surprising phenomenon can be explained by one of the theorems established. Copyright © 2018, The Authors. All rights reserved.

关键词： Matrix algebra

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Vehicle re-identification by fusing multiple deep neural networks

Vehicle re-identification by fusing multiple deep neural net...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Chao Cui Nong Sang Changxin Gao Lei Zou Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Vehicle re-identification has become a fundamental task because of the growing explosion in the use of surveillance cameras in public security. The most widely used solution is based on license plate verification. But when facing the vehicle without a license, deck cars and other license plate information error or missing situation, vehicle searching is still a challenging problem. This paper proposed a vehicle re-identification method based on deep learning which exploit a two-branch Multi-DNN Fusion Siamese Neural Network (MFSNN) to fuses the classification outputs of color, model and pasted marks on the windshield and map them into a Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. In order to achieve this goal, we present a method of vehicle color identification based on Alex net, a method of vehicle model identification based on VGG net, a method of pasted marks detection and identification based on Faster R-CNN. We evaluate our MFSNN method on VehicleID dataset and in the experiment. Experiment results show that our method can achieve promising results.

关键词： image color analysis Feature extraction Neural networks Task analysis Licenses Automobiles Automotive components

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Motion deblurring based on convolutional neural network 12th

Motion deblurring based on convolutional neural network

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

作者： Tan, Yunfei Zhang, Di Xu, Fei Zhang, Danyang Department of Physical Education Huazhong University of Science and Technology WuhanHubei430074 China Key Laboratory of Image Information Processing Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China JamaicaNY11451 United States

ISBN: (纸本)9789811071782

Object motion blur results when the object in the scene moves during the recording of a single exposure, either due to too rapid movement or long exposure, leaving streaks of the moving object in the image and thus degrading its quality. In this paper, we present a method to solve the object motion blur problem in images with clear static background. Specifically, we propose an object motion deblurring algorithm that uses a convolutional neural network with six convolutional layers to deblur the image. Taking advantages of the strong ability of feature learning in convolutional neural networks, our method can remove the blurring effect of fast-moving object while keeping the clear background untouched. It is well known that neural networks are best driven by large data sets and more data means more benefits for training convolutional neural networks;therefore, we generated training set of 144,000 images and test set of 32,400 images. Through carefully designed training process, our model learned the ability of deblurring the blurred object while keeping the clear background. The experiment results show that our approach can generate superior results to a representative image deblurring algorithm that treats the same blurred object and clear background. © Springer Nature Singapore Pte Ltd 2017.

关键词： image enhancement

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Region stability analysis and tracking control of memristive recurrent neural network

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Neural networks : the official journal of the International Neural Network Society 2018年 98卷 51-58页

作者： Gang Bao Zhigang Zeng Yanjun Shen Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: ctgugangbao@***. China School of Automation Huazhong University of Science and Technology Wuhan Hubei 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: shenyj@***.

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) lab.ratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result.

关键词： Memristive neural network Memristor Stability

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Hierarchical P systems with randomized right-hand sides of rules 18th

Hierarchical P systems with randomized right-hand sides of r...

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18th International Conference on Membrane Computing, CMC 1018

作者： Alhazov, Artiom Freund, Rudolf Ivanov, Sergiu Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5 Chişinău2028 Moldova Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China Faculty of Informatics TU Wien Favoritenstraße 9–11 Vienna1040 Austria LACL Université Paris Est – Créteil Val de Marne 61 av. Général de Gaulle Créteil94010 France TIMC-IMAG/DyCTiM Faculty of Medicine of Grenoble 5 avenue du Grand Sablon La Tronche38700 France

ISBN: (纸本)9783319733586

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We define three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. © 2018, Springer International Publishing AG.

关键词： Hierarchical systems

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Proxy-based Sliding Mode Stabilization of a Class of Second-order Nonlinear System

Proxy-based Sliding Mode Stabilization of a Class of Second-...

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Asian control Conference

作者： Guangzheng Ding Jian Huang Bin Hu Zhi-Hong Guan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei P.R. China

ISBN: (纸本)9781509015740;9781509015733

The essential feature of conventional proxy-based sliding mode control (PSMC) method is the introduction of a proxy, which is controlled by a normal sliding mode control (SMC) approach to track the desired trajectory. Both the safety problem in conventional stiff position control and the chattering problem in the SMC are overcome by the PSMC strategy. Meanwhile, the stability problem of PSMC is not well addressed for general nonlinear systems. In this paper, a new PSMC method is proposed for robust tracking control of a class of second-order nonlinear systems. A PD type virtual coupling is used and a specified sliding mode controller is designed in the proposed PSMC method. Based on the model of a class of second-order nonlinear systems, the stability of the closed-loop PSMC system is proved by Lyapunov theorem. Numerical simulations were carried out to verify the propose method.

关键词： Nonlinear systems Force Couplings Stability analysis Robustness Sliding mode control Safety

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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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Stability Analysis for Memristive Recurrent Neural Network Under Different External Stimulus

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Neural processing Letters 2017年第2期47卷 601-618页

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

Memristor is the fourth missing element. This paper discusses dynmacis memristive recurrent neural network with memristors as synapses. Firstly, it analyzes variation property of memristance under different external inputs with memristor simulation model. It concludes that memristance will be stable at one value if the direction of voltage is not changed and be varying periodically under periodically variable voltage. Next, it presents the memristive recurrent neural network model and gives local attractive region, one sufficient condition for memristive recurrent neural network under periodic voltage source. At last, an illustrative example is given for verifying our result.

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

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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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