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An efficient nondominated sorting algorithm 18

An efficient nondominated sorting algorithm

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Wang, Junchen Li, Changhe Diao, Yiya Zeng, Sanyou Wang, Hui School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781450357647

Nondominated sorting (NS) is commonly needed in multi-objective optimization to distinguish the fitness of solutions. Since it was suggested, several NS algorithms have been proposed to reduce its time complexity. In our study, we found that their performances are closely related to properties of the distribution of a data set, especially the number of fronts. To address this issue, we propose a novel NS algorithm Filter Sort. We also propose a new benchmark data generator for evaluating the performance of a NS algorithm. Experimental results show that our algorithm is superior to several state-of-the-art NS algorithms in most cases. © 2018 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Extended dissipativity analysis of delayed memristive neural networks based on a parameter-dependent lyapunov functional

Extended dissipativity analysis of delayed memristive neural...

引用

2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Lu, Chengda Zhang, Xian-Ming Wu, Min Han, Qing-Long He, Yong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9781538666173

This paper is concerned with extended dissipativity analysis of memristive neural networks with time-varying delays. Using the characteristic function technique, a tractable model of a memristive neural network is obtained. This model is similar to a neural network with polytopic uncertain synaptic weights, enabling us to construct a parameter-dependent Lyapunov functional. By combining this functional and some integral inequalities, a novel extended dissipativity criterion is obtained in terms of linear-matrix-inequalities, where different Lyapunov matrices are used for each form of the memristive neural network. Through a numerical example, this criterion is shown to be less conservative than the one based on a common Lyapunov functional. © 2018 IEEE.

关键词： Linear matrix inequalities

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Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

Speech Emotion Recognition Based on Linear Discriminant Anal...

引用

第37届中国控制会议

作者： Jun-Wei Mao Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For speech emotion recognition,emotional feature set with high dimension may produce redundant features and influence the recognition *** solve this problem and obtain the optimal emotional feature subset of speech,a feature dimension reduction based on linear discriminant analysis is *** to the confusion degree between different basic emotions,an emotion recognition method based on support vector machine decision tree is *** on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented,where standard feature sets of the INTERSPEECH and classic classifiers are used in comparative experiments *** results show that the proposal achieves 84.39%recognition accuracy on *** proposal,it would be fast and efficient to discriminate emotional states of diverse speakers from speech,and it would make it possible to realize the interaction between speaker and computer/robot in the future.

关键词： Emotion Recognition Speech Linear Discriminant Analysis Support Vector Machine Decision Tree

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Research and Implementation of Target Tracking Algorithm Based on Convolution Neural Network

Research and Implementation of Target Tracking Algorithm Bas...

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第37届中国控制会议

作者： LiJun Zhang Chen Wang Xing Jin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence of in-depth learning methods provide a new opportunity for the study of video target tracking. This paper first analyzes the research problems of video target tracking at present, analyzes the characteristics and trends of video target tracking in the new period, introduces the emerging recursive neural network frame structure, combined with Kalman filter And the experimental results show that the accuracy and robustness of the target tracking based on the convolution neural network algorithm are all good.

关键词： Regional convolutional neural network. Target tracking Kalman filtering

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Prediction of ROP and MPV Based on Support Vector Regression Method

Prediction of ROP and MPV Based on Support Vector Regression...

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第37届中国控制会议

作者： Yang Zhou Xin Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

During the drilling process, accurate prediction of drilling efficiency and safety plays a key role in timely adjustment of drilling process state. In general, surface parameters rate of penetration（ROP） and mud pit volume（MPV） are often used as important parameters to judge drilling safety and efficiency due to the bad bottom hole environment and unreliable detection devices. However, most drilling systems are underground, the structure is complex and exists many disturbances, so the state of drilling process is difficult to accurately predict. In this paper, an online support vector regression（OSVR） model is proposed to predict the ROP and MPV. First, the parameters of the model are determined by simple drilling process analysis. Then, the fast fourier transform filtering method is used to filter the high frequency disturbances of the data. Finally, the prediction model is established by support vector regression（SVR） method and the model is continuously updated by the model update method. The simulation results of industrial data show that the proposed model has a good prediction effect.

关键词： Rate of Penetration Mud Pit Volume Drilling Process SVR Method

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Multi-Objective Drilling Trajectory Optimization with A Modified complexity Index

Multi-Objective Drilling Trajectory Optimization with A Modi...

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第37届中国控制会议

作者： Wendi Huang Min Wu Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Drilling trajectory optimization is an important part before drilling process. Since decreasing the cost and increasing the safety of drilling process are contrary to each other, drilling trajectory optimization problems should be modeled as multiobjective optimization problems. For this purpose, proposing appropriate optimization index which meet the requirement of drilling process is necessary. Many researches applied drill-string torque as the safety index. However, the actual drilling trajectory may deviate from the design trajectory. Ignoring this fact may cause the torque prediction too optimistic. In this research, the drill-string torque is combined with tortuosity of drilling trajectory to reduce the optimism of the prediction of drillstring torque. A 3D drilling trajectory optimization problem is formulated as a multi-objective optimization problem, and the objective functions are drilling trajectory length and the modified drill-string torque. Non-dominated sorting genetic algorithm II is applied to solve the multi-objective optimization problem, and optimal pareto set are obtained.

关键词： Drilling trajectory multi-objective optimization drill-string torque

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Study on Maximum Power Point Tracking Based on Parabolic Approximation Method for Photovoltaic Power Generation System

Study on Maximum Power Point Tracking Based on Parabolic App...

引用

第37届中国控制会议

作者： Rengui Li Zhili Tan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Maximum power point tracking controller is essential to obtain the maximum power from a solar array in the photovoltaic systems as the PV power module varies with the temperature and solar irradiation. In the DC/DC circuit, the maximum power point tracking algorithm based on parabolic approximation method is used. On the basis of analyzing the principle of various tracking methods, the key technology of parabola approximation can be found to find the exact maximum power point.

关键词： Parabolic approximation method Maximum power point tracking MPPT Simulation of MATLAB

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control strategy based on iterative method for planar Pendubot

Control strategy based on iterative method for planar Pendub...

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第37届中国控制会议

作者： Zixin Huang Xuzhi Lai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a position control strategy based on the iterative method for a planar *** control objective of the system is to move the end-point from any initial equilibrium point to a target equilibrium *** presented method is based on the iterative steering,where a converging control law is applied *** order to compute such a control law,the dynamic equations of the system are transformed via partial feedback linearization and nilpotent ***,the simulation results demonstrate that the position control objective is realized by using this control strategy.

关键词： Planar Pendubot Position control Iterative method Nilpotent approximation

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Hyperparallelepiped Based Loop partitioning of Adaptive Dynamic Programming Structure

Hyperparallelepiped Based Loop partitioning of Adaptive Dyna...

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第37届中国控制会议

作者： Lu Li Xin Chen Hao Fu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper focuses on an accelerating method for partitioning the loops in the structure of the adaptive dynamic programming（ADP）. ADP contains critic-actor structure which involves the iterations of the value function. When the system needs to be stable, the value function generally needs to iterate thousands of times, the high computation burden which hinders the iterations will be generated. In order to reduce the computation burden, we introduce a hyperparallelepiped based loop partitioning（H-LP） method which splits the iterations of the value function and reduces the communication traffic calculated by the data footprint. The experiment results show that the computation performance will be enhanced when the H-LP method is introduced. The proposed method has an important practical significance.

关键词： Hyperparallelepiped Loop Partitioning Adaptive Dynamic Programming

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Identification of Essential Proteins Using Improved Node and Edge Clustering Coefficient

Identification of Essential Proteins Using Improved Node and...

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第37届中国控制会议

作者： Zhu Yuan Wu Chong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Computational methods are often applied to identify essential proteins from protein-protein interaction networks. In this paper, inspected by node and edge clustering coefficient（NEC） and Pe C, we propose an improved version of node and edge clustering coefficient（INEC） which both considers dual topological characteristics of the network and high false positives of the protein-protein interaction data. We apply it for the identification of essential proteins. And we implement three versions of INEC which combine different biological information. In order not to be confused, we call the first one INEC0 which dosen’t integrate biological information, the second one INEC1 which integrates gene expression similarity, and the third one INEC2 which integrates gene expression similarity, functional similarity, and protein-protein sequence similarity. We apply three implemented INEC methods to protein-protein interaction data of Saccharomyces cerevisiae（Yeast） and compare them with some state-of-theart methods（DC, NC, Pe C, and NEC）. The experimental results show that our proposed methods achieve better results in terms of prediction accuracy, area under the curve of PR-curve, and Jackknife methodology.

关键词： Protein-Protein Interaction Essential Protein Protein-Protein Interaction Network Biological Information

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