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Stability Analysis for Memristive Recurrent Neural Network Under Different External Stimulus

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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Duplicate checking Strategies for Selecting Topics for Graduation Thesis Based on Maximum Public Sequence

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IOP Conference Series: Earth and Environmental Science 2019年第3期267卷

作者： Guorui Yu Li Huang Jiewen Sun Shangchun Liao School of Computer Science and Technology Wuhan University of Science and Technology phone:*** Intelligent Information Processing and Hubei Real-time Industrial Systems Laboratory. Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology Ministry of Education.

The system is based on the undergraduate thesis management system as the practice platform, focusing on the realization of the thesis topic check function. The current check-up detection system is based on the entire contents of the query, and does not meet the requirements for an undergraduate thesis query. According to the characteristics of the thesis, the system first divides the keywords into the topic, selects the irrelevant parts, then removes the irrelevant words, and finally checks the keywords in the topic, and realizes the efficient and accurate check for inspection.

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Multi-objective optimization for ladle tracking of aluminium tapping based on NSGA-II 1

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

作者： Zhou, Kaibo Zou, Yutao Wang, Hongting Xu, Gaofeng Guo, Sihai School of Automation Huazhong University of Science and Technology Wuhan430074 China MOE Key Laboratory of Image Processing and Intelligence Control Wuhan430074 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

In order to realize the optimization of ladle tracking of aluminium tapping, a mathematical model, which takes the grade of aluminium, the energy required for transportation and the optimum ratio of aluminium liquid into account, is established. The traditional method optimizes the impurity content and the transport distance based on a single objective optimization, but it requires the empirical values of the weight coefficients. The paper proposes a modified multi-objective optimization model with the elitist non-dominated sorting genetic algorithm (NSGA-II). In the crossover operator process and the mutation operator process, the separately improved methods are introduced based on the ladle tracking problem of aluminium tapping, which replaces the Simulated Binary Crossover (SBX) in the original NSGA-II algorithm into Partially Matched Crossover (PMX) based on natural number coding and uses exchange mutation (EM) operator. Finally, the practical production data of the aluminium factory is used to verify the validity and practicability of the method, and the results show that this method can obtain a feasible solution for the user to choose suitable solutions, and avoid the defects of selecting empirical weighting coefficient. © Springer Nature Singapore Pte Ltd 2017.

关键词： Multiobjective optimization

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Fault diagnosis in aluminium electrolysis using a joint method based on kernel principal component analysis and support vector machines 12th

Fault diagnosis in aluminium electrolysis using a joint meth...

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

作者： Zhou, Kaibo Xu, Gaofeng Wang, Hongting Guo, Sihai School of Automation Huazhong University of Science and Technology Wuhan430074 China MOE Key Laboratory of Image Processing and Intelligence Control Wuhan430074 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789811071782

As a key part of aluminium smelting, the operational conditions of aluminium electrolytic cells are of great significance for the stability of the aluminium electrolysis process. As a result, developing a effective process monitoring and multiple fault diagnosis model is essential. Traditional multi-classification methods such as neural networks and multiple support vector machines (multi-SVM) have good effects. However, the connatural limitations of these methods limit the prediction accuracies. To solve this problem, a hierarchical method for multiple fault diagnosis based on kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed in this paper. Firstly, test statistics, such as the comprehensive index , the squared prediction error (SPE), and Hotellings T-squared (T2), are used for fault detection. To separate faults preliminarily, traditional K-means clustering as transition layer is applied to the principal component scores. Next, anode effect is recognized and classified by the established SVM prediction model. Compared with multi-SVM-based classification methods, the proposed hierarchical method can diagnosis different faults with a higher precision. The prediction accuracy can reach about 90%. © Springer Nature Singapore Pte Ltd 2017.

关键词： Support vector machines

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Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 177-185页

作者： Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects *** the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor *** research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral *** involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition *** maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects *** performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关键词： distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification

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Learning data-adaptive nonparametric kernels

arXiv

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arXiv 2018年

作者： Liu, Fanghui Huang, Xiaolin Gong, Chen Yang, Jie Li, Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Automation Tsinghua University

Kernel methods have been extensively used in a variety of machine learning tasks such as classification, clustering, and dimensionality reduction. For complicated practical tasks, the traditional kernels, e.g., Gaussian kernel and sigmoid kernel, or their combinations are often not sufficiently flexible to fit the data. In this paper, we present a Data-Adaptive Nonparametric Kernel (DANK) learning framework in a data-driven manner. To be specific, in model formulation, we impose an adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we do not specify the formulation of the adaptive matrix, each entry in the adaptive matrix can be directly and flexibly learned from the data. Therefore, the solution space of the learned kernel is largely expanded, which makes our DANK model flexible to capture the data with different local statistical properties. Specifically, the proposed kernel learning framework can be seamlessly embedded to support vector machines (SVM) and support vector regression (SVR), which has the capability of enlarging the margin between classes and reducing the model generalization error. Theoretically, we demonstrate that the objective function of our DANK model embedded in SVM/SVR is gradient-Lipschitz continuous. Thereby, the training process for kernel and parameter learning in SVM/SVR can be efficiently optimized in a unified framework. Further, to address the scalability issue in nonparametric kernel learning framework, we decompose the entire optimization problem in DANK into several smaller easy-to-solve problems, so that our DANK model can be efficiently approximated by this partition. The effectiveness of this approximation is demonstrated by both empirical studies and theoretical guarantees. Experimentally, the proposed DANK model embedded in SVM/SVR achieves encouraging performance on various classification and regression benchmark datasets when compared with other representative kernel learning based algorithms. Copyrig

关键词： Matrix algebra

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Research on weak signal detection based on adaptive multi-stable stochastic resonance under α stable noise

Research on weak signal detection based on adaptive multi-st...

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Annual Conference of Industrial Electronics Society

作者： Shang-bin Jiao Yi Wang Qing Zhang Le Fan Di Sun Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

In this paper, the influences of system parameters on stochastic resonance output effect is analyzed, which takes the multi-stable stochastic resonance system as the model. That is, the vibration condition of multi-stable stochastic resonance system, and weak signal detection method based on the multi-stable stochastic resonance under α stable noise is investigated. Then considering the real-time detection of weak signals in practical engineering, the multi-stable stochastic resonance system parameters a, b, c are optimized by particle swarm optimization(PSO), which takes the output signal-to-noise ratio(SNR out ) as the fitness function. Finally, multi-frequency weak signals detection with α stable noise is achieved, and the above method is applied to the vibration fault diagnosis of turbine. Both simulation and experiment results show that this method can quickly and effectively detect multi-frequency weak signals submerged in strong noise background, which lays a foundation for its application in engineering practice.

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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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Massive MIMO Channel Modeling Using Map-based Ray Tracing Method

Massive MIMO Channel Modeling Using Map-based Ray Tracing Me...

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2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies

作者： Junliang Yao Haipeng Ren Qing Liu Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi'an University of Technology

Massive multiple-input multiple-output(MIMO), with giant array size and multi-dimension array structure, has been widely considered as a key physical layer technique in future wireless communications. With regarding the large number of antenna elements, some new challenges and issues are arising, e.g. modeling the near-field effects in MIMO channel and the high computational complexity. To solve these problems, a statistical channel model is proposed based on the cluster delay line(CDL) framework. The proposed model focuses on the spherical wavefront theory, thus can be applied to the massive MIMO system. Furthermore, the map-based ray tracing algorithm with low complexity is used to compute the statistical parameters, such as pathloss, delay spread, etc., The numerical analysis results show that the proposed channel model is enable to describe the main characteristics of massive MIMO channel.

关键词： large scale MIMO wireless channel modeling ray tracing algorithm path loss delay spread

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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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