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Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices A Hybrid Assessment Method for Evaluating the Performance of Starting Pitchers in a Professional Baseball Team 请帮忙查询以上两篇文章是否ISTP检索？谢谢 |
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【答案】应助回帖
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coocicat(杈杈代发): 金币+15, 感谢提供详细的检索信息！ 2013-10-15 09:06:12
杈杈: 回帖置顶 2013-10-15 09:06:14
coocicat(杈杈代发): 金币+15, 感谢提供详细的检索信息！ 2013-10-15 09:06:12
杈杈: 回帖置顶 2013-10-15 09:06:14
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1 Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices 作者: Lu, JN (Lu, Jinna)[ 1 ] ; Bai, YP (Bai, Yanping) 编者: Zhang, L; Li, X; Chen, J 来源出版物: PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON INFORMATION, BUSINESS AND EDUCATION TECHNOLOGY (ICIBET 2013) 丛书: Advances in Intelligent Systems Research 卷: 26 页: 69-72 出版年: 2013 被引频次: 0 (来自 Web of Science) 引用的参考文献: 9 [ 查看 Related Records ] 引证关系图 会议: International Conference on Information, Business and Education Technology (ICIBET) 会议地点: Beijing, PEOPLES R CHINA 会议日期: MAR 14-15, 2013 摘要: Generalized regression neural network (GRNN) has very good effect on making nonlinear forecasting model with large number of stock data. Particle swarm optimization (PSO) has simple operation analysis and is easy to implement. We use PSO algorithm to optimize the GRNN in order for optimal smoothing factor and connection weights. The prediction errors of the two models are both small. The MSE error by GRNN model reaches 0.0486, while the error by PSO-GRNN model is 0.0104. The analysis shows that PSO-GRNN model is more accurate, more stabilized and more generic than GRNN model. 入藏号: WOS:000320283600015 文献类型: Proceedings Paper 语种: English 作者关键词: generalized regression; Particle Swarm Optimization; neural network model; Stock Price Prediction 通讯作者地址: Lu, JN (通讯作者) North Univ China, Mailbox 722,3 Xueyuan RD, Taiyuan 030051, Shanxi Prov, Peoples R China. 地址: [ 1 ] North Univ China, Taiyuan 030051, Shanxi Prov, Peoples R China 电子邮件地址: jinna4813@gmail.com; baiyp666@163.com 出版商: ATLANTIS PRESS, 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE Web of Science 类别: Computer Science, Artificial Intelligence; Computer Science, Information Systems 研究方向: Computer Science IDS 号: BFK72 ISSN: 1951-6851 ISBN: 978-90-78677-57-42 2 A Hybrid Assessment Method for Evaluating the Performance of Starting Pitchers in a Professional Baseball Team 作者: Chen, CC (Chen, Chih-Cheng)[ 1 ] ; Lee, YT (Lee, Yung-Tan); Tsai, CM (Tsai, Chung-Ming) 编者: Zhang, L; Li, X; Chen, J 来源出版物: PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON INFORMATION, BUSINESS AND EDUCATION TECHNOLOGY (ICIBET 2013) 丛书: Advances in Intelligent Systems Research 卷: 26 页: 73-76 出版年: 2013 被引频次: 0 (来自 Web of Science) 引用的参考文献: 8 [ 查看 Related Records ] 引证关系图 会议: International Conference on Information, Business and Education Technology (ICIBET) 会议地点: Beijing, PEOPLES R CHINA 会议日期: MAR 14-15, 2013 摘要: The performance assessment of professional baseball starting pitchers is considered a multi-attribute decision-making (MADM) problem. This study develops an evaluation model based on the analytic hierarchy process (AHP) and grey relational analysis (GRA) to evaluate starting pitcher performance for teams of the Chinese Professional Baseball League. The AHP is used to determine attribute weights, whereas the GRA calculates the individual grey relational degree. We conducted an empirical analysis to show the use of the model for addressing the starting pitcher performance problem. The results demonstrate the effectiveness and feasibility of the proposed model. 入藏号: WOS:000320283600016 文献类型: Proceedings Paper 语种: English 作者关键词: Starting pitchers; AHP; GRA; Chinese Professional Baseball League KeyWords Plus: MODEL 通讯作者地址: Chen, CC (通讯作者) Aletheia Univ, Dept Sport Management, New Taipei City 25103, Taiwan. 地址: [ 1 ] Aletheia Univ, Dept Sport Management, New Taipei City 25103, Taiwan 出版商: ATLANTIS PRESS, 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE Web of Science 类别: Computer Science, Artificial Intelligence; Computer Science, Information Systems 研究方向: Computer Science IDS 号: BFK72 ISSN: 1951-6851 ISBN: 978-90-78677-57-4 |
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2楼2013-10-11 17:07:04
