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1902 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 Collaborative Filtering. ACM Transactions on Information Systems, Vol. 22, No. 1, January 2004, 116–142. [3] Liu Jianguo, Zhou Tao, et al. Overview of the Evaluated Algorithms for the Personal Recommendation Systems. Complex System and Complexity Science. 2009, Vol.6, No.3, 1-10. [4] Rong Jin, Luo Si, et al. Collaborative Filtering with Decoupled Models for Preferences and Ratings. CIKM ’03, New Orleans, Louisiana, USA, November 3-8, 2003. [5] Rich E. User modeling via stereotypes. Cognitive Science, 1979, 3(4):329-354. [6] Goldberg D, Nichols D, et al. Using collaborative filtering to weave an information tapestry. Comm ACM, 1992, 35(12):61-70. [7] Konstan JA, Miller BN, et al. GroupLens: Applying collaborative filtering to usenet news. Comm ACM, 1997, 40(3):77-87 [8] Shardanand U, Maes P. Social information filtering: Algorithms for automating ‗Word of Mouth‘. Proc Conf Human Factors in Computing Systems Denver, 1995, 210- 217. [9] Linden G, Smith B, et al. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing. 2003, 7(1):76-80. [10] Goldberg K, Roeder T, et al. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval. 2001, 4(2):133-151. [11] Terveen L, Hill W, et al. PHOAKS: A system for sharing recommendations. Comm ACM, 1997, 40(3):59-62. [12] J. S. Breese, D. Heckerman, et al. Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). 1998. [13] Chen YL, Cheng LC. A novel collaborative filtering approach for recommending ranked items. Expert Systems with Applications, 2008, 34(4):2396-2405. [14] Yang MH, Gu ZM. Personalized recommendation based on partial similarity of interests. Advanced Data Mining and Applications Proceedings, 2006, 4093:509-516. [15] Breese JS, Heckerman D, et al. Empirical analysis of predictive algorithms for collaborative filtering. Proc 14th Conf Uncertainty in Artificial Intelligence Madison, 1998, 43-52 [16] Getoor L, Sahami M. Using probabilistic relational models for collaborative filtering. Proc Workshop Web Usage Analysis and User Profiling, San Diego. 1999. [17] Billsus, D., Pazzani, M. J. Learning collaborative information filters. In Proceedings of the 15th International Conference on Machine Learning, 1998, 46– 54. [18] Schein, A. I., Popescul, A., et al. Methods and metrics for coldstart recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). (Tampere, Finland), 2002, 253–260. [19] Sarwar, B., Karypis, G., et al. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference. 2001, 285–295. [20] Sarwar, B., Karypis, G., et al. Application of dimensionality reduction in recommender systems: A case study. In Proceedings of the WebKDD Workshop at the ACM SIGKKD. ACM, New York.2000. [21] Good, N., Schafer, J., et al. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the 16th National Conference on Artificial Intelligence, 1999, 439–446. © 2011 ACADEMY PUBLISHER [22] Huang, Z., Chung, W., et al. A graph-based recommender system for digital library. In Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries (Portland, Ore.). ACM, New York, 2002, 65–73. [23] Chrsistian Desrosiers, George Karypis. Solving the Sparsity Problem: Collaborative Filtering via Indirect Similarities. Technical Report. Department of Computer Science and Engineering University of Minnesota 4-192 EECS Building 200 Union Street SE Minneapolis, MN 55455-0159 USA. 2008. [24] Sarwar, B., Karypis, G., et al. Analysis of recommendation algorithms for e-commerce. In Proceedings of the ACM Conference on Electronic Commerce. ACM, New York, 2000, 158–167. Yibo Chen, born in 1982, Ph.D. candidate. The research interests include personalization recommendation and semantic web. Chanle Wu, born in 1945, professor, The interests include computer networks, e-Learning, grid computing and semantic web. Ming Xie, born in 1978, Ph.D. candidate. The research interests include data mining and semantic web. Xiaojun Guo, born in 1984, Ph.D. candidate. The research interests include semantic web and e-Learning.
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1903 Integrated Structure and Control Design for Servo System Based on Genetic Algorithm and Matlab Dingzhen Li Department of Electronics and Electrical Engineering, Nanyang Institute of Technology, Henan 473004, China Email: lidingzhen.student@sina.com Ruimin Jin Department of Electronics and Electrical Engineering, Nanyang Institute of Technology, Henan 473004, China Email: jinruimin2004@163.com Abstract—The integrated design was introduced about the pitching part of airborne radar servo system. The paper analyzed both the servo system model and the dynamic character of this system. Then the research about parameter optimization and simulation had been done by using the Matlab. The electromechanical coupling model and the optimization model were built up based on model of mechanism transmission system and electricity control system. The optimization model includes the integrated design of structure parameters and control parameters. The dynamics model of mechanism transmission system includes the nonlinearity of backlash. It considered the influence of parameters for dynamics property in structure of the mechanism transmission system. Furthermore, the model of electricity control system of the airborne radar servo system has three feedback-control loops. The method of integrated structure and control design was applied on the optimization model using Genetic Algorithm (GA). Simulation had been done based on Matlab/Simulik. Simulation results showed that the method of integrated structure and control design is very good. It is feasible and effective for airborne radar servo system. It proved the method used in the task is right and the practicability of Genetic Algorithm. Index Terms—Radar Servo System, Integrated Design, Electromechanical Coupling Model, Genetic Algorithm, Matlab Simulation I. INTRODUCTION Modern mechanical and electrical systems have higher demanding for the system accuracy and steady-state dynamic performance in a variety of extreme conditions. The traditional design method is that the mechanical part is designed first, then the control part is designed. This method ignores the dynamics of the mechanical system, the interactions and the mutual coupling of the control system. The repeated design of the structural parameters and control parameters also causes the design cycle © 2011 ACADEMY PUBLISHER doi:10.4304/jcp.6.9.1903-1912 getting long and the cost increasing. It is also difficult to achieve the best performance of the electromechanical systems. In order to improve the overall performance of the system, we should unify the model to the mechanical and electrical systems and have an integrated design on the base of this. Namely, the integrated design of structure parameters and control parameters will be done. This paper studied the airborne radar pitch servo system. The first unit is a single stage gear meshing. We research the dynamics model of the pitch servo-mechanical drive system. The influence of the system dynamic characteristics is analyzed on the structural parameters of the mechanical transmission system. Secondly, the design of the loop circurt of servo system is introduced. Then we establish a servo-electric control system of this model and study the simulation model. Finally, the servo control system is coupled with mechanical system and electrical system. The mechanical structure is established including the structure parameters and control parameters of the electromechanical coupling model. Then we create an integrated optimization model for the structure/control integrated design. Airborne radar servo system includes machine system and electrical control system [1]. Traditional design methods all make the structure and control of radar servo system as a separate module, later the structure design is optimized, and then is added the optimal controller to the structure. It is sequential, serial. All parts are independent design, which ignore the strong coupling between the structural parameters and control parameters. It is very difficult to achieve the global optimum. To overcome this shortcoming, the design uses a method that makes the structure and control integrated. During the process of the design, we synchronize the optimization of structure parameters and control parameters. The simulation results show that the method can improve the synthetic performance of radar servo system effectively.
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Research on Self-built Digital Reso
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A Modified Technique for Analysis o
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