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Collaborative filtering algorithm via compressing the sparse user-rating-data matrix

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2009年第4期36卷 614-618+638页

作者： Hou, Cui-Qin Jiao, Li-Cheng Zhang, Wen-Ge Ministry of Education Key Lab. of Intelligent Perception and Image Understanding Research Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 China

The paper proposes a novel memory-based collaborative filtering algorithm-Multi-label Probabilistic Latent Semantic Analysis based Collaborative Filtering, which improves the quality of recommendations by reducing the dimension of the user-rating-data matrix by multi-label probabilistic latent semantic analysis when the matrix is extremely sparse. Firstly, it confines the set of latent variables of probability latent semantic analysis to the set of multi-label of items to make latent variables have meanings of corresponding labels. Then it learns the probabilistic distribution of latent variables, i.e., the model of use's interest, to compress the user-rating-data matrix. Finally, it computes the similarity between different users based on the above learned model and makes recommendations. Compared to memory-based collaborative filtering algorithms, the proposed algorithm decreases the mean absolute error 4 percents averagely on test dataset by reducing the dimension of the user-rating-data matrix. The proposed algorithm makes the recommendation system understandable and obtains competitive recommendations compared to the filtering algorithm which reduces the dimension of the user-rating-data matrix by probabilistic latent semantic analysis.

关键词： Iterative methods

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Displacement prediction model of landslide based on functional networks

Displacement prediction model of landslide based on function...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Chen, Jiejie Zeng, Zhigang Tang, Huiming Department of Control Science and Engineering 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 Faculty of Engineering China University of Geosciences Wuhan 430074 China

ISBN: (纸本)9783642390678

In this paper, a novel computational intelligence scheme is proposed to forecast landslide based on functional networks. Two types functional networks, general functional networks with two variables basis function (GFN) and separable functional networks (SFN) are applied to predict a real-world example. In addition, the experiments reveal that the landslide prediction using functional networks is reasonable and effective, and GFN are consistently better than SFN in terms of the same measurements. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Landslides

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Observer-Based Adaptive Tracking control of Rigid-Link Flexible-Joint Robot Manipulators

Observer-Based Adaptive Tracking Control of Rigid-Link Flexi...

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第二十九届中国控制会议

作者： Philippe POIGNET Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology

Joint flexibility is an important factor to consider in the robot control design if high performance is expected for the robot *** works on control of rigid-link flexible-joint(RLFJ)robot in literature have assumed that the kinematics of the robot is known *** have been few results that can deal with the kinematics uncertainty in RLFJ *** this paper,we propose an adaptive tracking control method which can deal with the kinematics uncertainty and uncertainties in both link and actuator dynamics of the RLFJ robot *** observers are designed to avoid accelerations measurement due to the fourth-order overall system *** stability of the closed-loop system is shown and sufficient conditions are presented to guarantee the stability.

关键词： Kinematics Uncertainty,Flexible Joint,Adaptive control,Robot Manipulator,Asymptotic Stability

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Event-based synchronisation of linear discrete-time dynamical networks

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IET control Theory and Applications 2015年第5期9卷 755-765页

作者： Chen, Michael Z.Q. Zhang, Liangyin Su, Housheng Li, Chanying Department of Mechanical Engineering University of Hong Kong Hong Kong School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science andTechnology Luoyu Road 1037 Wuhan430074 China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing100090 China

This study investigates the problem of event-based synchronisation of linear discrete-time dynamical networks. Leader-following and leaderless synchronisations are achieved by a distributed event-trigger strategy. It is shown that feedback control updating is unnecessary until an event is triggered. The combinational-state variables and the Riccati equation are used to construct a Liapunov function and to design the event-triggering conditions. Numerical examples are provided to illustrate the theoretical results. © The Institution of Engineering and Technology 2015.

关键词： Riccati equations

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Improving strategies on PSO for suborbit launch vehicle trajectory optimization

Improving strategies on PSO for suborbit launch vehicle traj...

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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Xie, Fuqiang Yang, Ye Chang, Songtao Wang, Yongji School of Electric Engineering University of Souch China Hengyang China Beijing Aerospace Automatic Control Institute Beijing China Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781612843735

Solving the optimal control problem with a free final time, such as suborbital launch vehicle (SLV) trajectory optimization with two control variables and multi-constraints ones based on particle swarm optimization (PSO), the smoothness of control variable can not be satisfied by linear interpolation method. A novel method including some improving strategies based on PSO for trajectory optimization is proposed, named LCPSO which is a kind of Cooperate PSO based on Legendre orthogonal polynomials. An additional control variable is introduced and transcribes the original optimal problem to a problem with fixed final time, and one dimension searching method based on interval analysis is used to optimize the additional control variable. Furthermore, a theorem on how to find the boundaries of the coefficient of polynomials is proved. Compared with basic PSO, LCPSO has traits of lower dimensions and smoother control variable. An example of trajectory optimization shows the effectiveness of the LCPSO algorithm. © 2011 IEEE.

关键词： Aerodynamics

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H ∞ based decoupling tracking control of hypersonic vehicle

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IFAC Proceedings Volumes 2014年第3期47卷 894-899页

作者： Xuejing Lan Zhishen Wang Lei Liu Yongji Wan School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

The H ∞ based decoupling tracking control is studied in this paper. A virtual system constituted by the controlled system and the no coupling reference model is firstly set up. The controlled system is driven to follow the reference model to realize the decoupling. And the tracking error can be formulated by the H ∞ norm of the virtual system. Then the controller is derived by minimizing the H ∞ norm, which can be described by Linear Matrix Inequalities (LMIs). The necessary and sufficient condition of existence of controller is derived based on the LMIs above. A flight control example is given to illustrate the effectiveness of the proposed method. The simulation results show that the proposed method is of better control performance than Linear Quadratic (LQ) tracking controller.

关键词： decoupling control reference model H ∞ LMI hypersonic vehicle

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Chaos analysis and control in a chaotic circuit with a PWL memristor

Chaos analysis and control in a chaotic circuit with a PWL m...

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2011 International Conference on Information Science and Technology, ICIST 2011

作者： Wen, Shiping Shen, Yi Zeng, Zhigang Cai, Yanxia Department of Control Science and Engineering 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 Fenghuang Middle School Xinzhou Wuhan Hubei 430074 China

ISBN: (纸本)9781424494408

In this paper, a nonlinear chaotic system is presented, which is derived from the chua's circuit with a memristor. This is a four-dimensional autonomous circuit which exhibits chaotic behavior. The chaotic system is demonstrated by Lyapunov exponent, and phase portraits. Furthermore, this system can be stabilized by a switched feedback controller which can be obtained by solving a set of LMIs. And the results will be demonstrated by the numerical simulation. © 2011 IEEE.

关键词： Memristors

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Refrigerant capacity detection of dehumidifier based on time series and neural networks 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Peng, Gang Yang, Zuhuang Wang, Min School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan China Shenzhen Institute Huazhong University of Science and Technology Shenzhen518060 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Refrigerant leakage is common in the use of dehumidifier, leading to decrease of dehumidification ability. To detect refrigerant capacity effectively and maintain it timely, a neural network with deep learning skills was proposed based on time series. As input of networks, the time series consists of the operating parameters of the dehumidifier at multiple time points. To determine the refrigerant capacity, the proposed method combines the outputs on the neural networks of all the time series examples in a single run of dehumidifier. As the result suggests, the proposed method is capable of detecting the refrigerant capacity with a low missing rate and high accuracy, which improves the maintenance mechanism of the dehumidifier and is of great significance to ensuring a comfortable air environment for user. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

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An improved artificial bee colony algorithm and its taguchi analysis 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Ni, Yudong Li, Yuanyuan Shen, Yindong School of Mathematics Hefei University of Technology Hefei230009 China School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

The artificial bee colony (ABC) algorithm is one of well-known evolutionary algorithms, which has been successfully applied to many continuous or combinatorial optimization problems. To increase further its convergence speed and avoid being trapped in local optimum, this paper proposes an improved ABC algorithm (IABC), which aims to enhance diversification of search at each stage of the ABC algorithm. Firstly, a chaotic mapping rule is established by introducing a chaos operator into the initial position generation rules in order to ensure the ergodicity of initial positions. Then, an isometric contraction parallel search rule is devised, based on which a neighborhood search on initial positions is performed to enhance the convergence speed and the local search ability. Next, a parallel selection strategy is developed by using roulette and reverse roulette simultaneously, which allows selecting poor positions to escape from local optimum. Meanwhile, a global updating mechanism based on gravitational potential field is developed, which can guide the rejection and generation of positions to accelerate the convergence of the algorithm. The computational results show that the IABC can improve the convergence speed and solution quality without falling into the local optimum prematurely. Finally, a further analysis on the IABC is conducted using the Taguchi method, which focuses on the factor level setting related to the following key factors: chaotic mapping rules in the initial position generation rules, isometric contraction parallel search rules, parallel selection strategies and the update threshold in the global updating mechanism. The results display that the optimal combination of factor levels has been achieved in the IABC. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Taguchi methods

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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