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coocicat(è¾è¾´ú·¢): ½ð±Ò+5, лл£¡ 2013-10-15 09:06:25
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coocicat(è¾è¾´ú·¢): ½ð±Ò+15, ¸ÐлÌṩÏêϸµÄ¼ìË÷ÐÅÏ¢£¡ 2013-10-15 09:06:12
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1 Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices ×÷Õß: Lu, JN (Lu, Jinna)[ 1 ] ; Bai, YP (Bai, Yanping) ±àÕß: Zhang, L; Li, X; Chen, J À´Ô´³ö°æÎï: PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON INFORMATION, BUSINESS AND EDUCATION TECHNOLOGY (ICIBET 2013) ´ÔÊé: Advances in Intelligent Systems Research ¾í: 26 Ò³: 69-72 ³ö°æÄê: 2013 ±»ÒýƵ´Î: 0 (À´×Ô Web of Science) ÒýÓõIJο¼ÎÄÏ×: 9 [ ²é¿´ Related Records ] ÒýÖ¤¹ØÏµÍ¼ »áÒé: International Conference on Information, Business and Education Technology (ICIBET) »áÒ鵨µã: Beijing, PEOPLES R CHINA »áÒéÈÕÆÚ: MAR 14-15, 2013 ÕªÒª: 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. Èë²ØºÅ: WOS:000320283600015 ÎÄÏ×ÀàÐÍ: Proceedings Paper ÓïÖÖ: English ×÷Õ߹ؼü´Ê: generalized regression; Particle Swarm Optimization; neural network model; Stock Price Prediction ͨѶ×÷ÕßµØÖ·: Lu, JN (ͨѶ×÷Õß) North Univ China, Mailbox 722,3 Xueyuan RD, Taiyuan 030051, Shanxi Prov, Peoples R China. µØÖ·: [ 1 ] North Univ China, Taiyuan 030051, Shanxi Prov, Peoples R China µç×ÓÓʼþµØÖ·: jinna4813@gmail.com; baiyp666@163.com ³ö°æÉÌ: ATLANTIS PRESS, 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE Web of Science Àà±ð: Computer Science, Artificial Intelligence; Computer Science, Information Systems Ñо¿·½Ïò: Computer Science IDS ºÅ: 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 ×÷Õß: Chen, CC (Chen, Chih-Cheng)[ 1 ] ; Lee, YT (Lee, Yung-Tan); Tsai, CM (Tsai, Chung-Ming) ±àÕß: Zhang, L; Li, X; Chen, J À´Ô´³ö°æÎï: PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON INFORMATION, BUSINESS AND EDUCATION TECHNOLOGY (ICIBET 2013) ´ÔÊé: Advances in Intelligent Systems Research ¾í: 26 Ò³: 73-76 ³ö°æÄê: 2013 ±»ÒýƵ´Î: 0 (À´×Ô Web of Science) ÒýÓõIJο¼ÎÄÏ×: 8 [ ²é¿´ Related Records ] ÒýÖ¤¹ØÏµÍ¼ »áÒé: International Conference on Information, Business and Education Technology (ICIBET) »áÒ鵨µã: Beijing, PEOPLES R CHINA »áÒéÈÕÆÚ: MAR 14-15, 2013 ÕªÒª: 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. Èë²ØºÅ: WOS:000320283600016 ÎÄÏ×ÀàÐÍ: Proceedings Paper ÓïÖÖ: English ×÷Õ߹ؼü´Ê: Starting pitchers; AHP; GRA; Chinese Professional Baseball League KeyWords Plus: MODEL ͨѶ×÷ÕßµØÖ·: Chen, CC (ͨѶ×÷Õß) Aletheia Univ, Dept Sport Management, New Taipei City 25103, Taiwan. µØÖ·: [ 1 ] Aletheia Univ, Dept Sport Management, New Taipei City 25103, Taiwan ³ö°æÉÌ: ATLANTIS PRESS, 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE Web of Science Àà±ð: Computer Science, Artificial Intelligence; Computer Science, Information Systems Ñо¿·½Ïò: Computer Science IDS ºÅ: BFK72 ISSN: 1951-6851 ISBN: 978-90-78677-57-4 |
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