[1]吴建生.基于粒子群算法的神经网络短期降水预报建模研究[J].智能系统学报,2006,1(02):67-73.
 WU Jian-sheng.Study on the shorttime rainfall prediction model of neural ensemble based on PSO algorithms[J].CAAI Transactions on Intelligent Systems,2006,1(02):67-73.
点击复制

基于粒子群算法的神经网络短期降水预报建模研究(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第1卷
期数:
2006年02期
页码:
67-73
栏目:
学术论文—智能系统
出版日期:
2006-10-25

文章信息/Info

Title:
Study on the shorttime rainfall prediction model of neural ensemble based on PSO algorithms
文章编号:
1673-4785(2006)02-0067-07
作者:
吴建生
柳州师范高等专科学校数学与计算机科学系,广西柳州545004
Author(s):
WU Jian-sheng
Department of Mathematics and Computer Science, Liuzhou Teache rs College, Liuzhou 545004,China
关键词:
神经网络集成粒子群优化二次规划
Keywords:
neural network ensemble particle swarm optimization quadratic program
分类号:
TP183
文献标志码:
A
摘要:
用多样性粒子群算法优化神经网络的网络结构和连接权,获得神经网络集成个体;进一步用二次规划方法,计算各集成个体的最优非负权系数进行组合集成,生成神经网络集成的输出结论,进行短期降水预报建模研究.以广西全区的月降水量实例分析,结果表明该方法能有效提高系统的泛化能力.
Abstract:
This paper presents the evolving neural network architecture and connection weig hts based on Diversityguided Particle Swarm Optimization algorithms. The ensem ble strategy is carried out by using the quadratic programming to calculate the best nonnegative weights. The weighted coefficient of each ensemble individual is o b tained. This method can be used to establish the forecast model of the shortti m e rainfall. The applied example is built with the monthly mean rainfall in the w hole area of Guangxi. The result shows that this method can effectively increase the generalization ability of neural network. 

参考文献/References:

[1] DEAN A, ANDREW R, BRIAN H. Forecasting warmseason burn o ff low clouds at the San Francisco international airport using linear regression a nd a neural network [J]. Apply Meteor, 2002, 41(6): 629-639.
[2]HSIEH W W. Nonlinear canonical correlation analysis of the tropical pacifi c climate variability using neural network approach[J]. Apply Meteor,2001,14(12) : 2528-2539. 
[3]GRIORGIO CORANI, GIOGIO GUARIO. Coupling fuzzy modeling and neural network s for river flood prediction [J]. IEEE Transactions on Systems, Man, And Cyber neticPart C: Applications and Reviews, 2005, 25(3): 382-388.
[4]吴建生,金 龙, 农吉夫. 基于遗传算法的神经网络建模研究[J]. 数学的实践与认识, 2005, 35(1): 83-88.
 WU Jiansheng,JIN long,LONG Jifu. Study on model of neural network based on genet ic algorithm[J].Mathematics in Practic and theory,2005,35(1):83-88.
[5]SOLLICH P, KROGH A. Learning with ensembles: how overfitting can be usef ul[A]. In Proceeding of Advances in Neural Information Processing Systems 8[C ]. Cambridge, MA: MIT Press, 1996.
[6]HANSEN L K, SALAMON P. Neural network ensembles [J]. IEEE Transaction s on Pattern Analysis and Machine Intelligence, 1990,12(10): 993-1001.
[7]周志华, 陈世福. 神经网络集成[J]. 计算机学报, 2002, 25(1): 1-8. ZHOU Zhihua,CHEN Shifu. Neural nctwork ensemble[J].Chinese Journal of Computer , 2002,25(1):1-8.
[8]MAO J. A case study on bagging boosting and basic ensembles of neur al networks for OCR[A]. In Proceedings of International Joint conference on Ne ural Networks 1998[C]. Anchorage, AK, 1998:1828-1833.
[9]GUTTA S, WECHSLER H. Face recognition using hybrid classifier systems[A ]. In Proceedings of International Conference Neural Network 1996[C]. Washin gton, DC, 1996:1017-1022.
[10]SOLLICH P, INTRATOR N. Classification of seismic signals by integrating ensembles of neural networks [J]. IEEE Transactions Signal Processing,1998, 46(5):1194-1021.
[11]BONABEAU E, DORIGO M, G THERAULAZ. Inspiration for optimization from s ocial insect behavior [J]. Nature, 2000, 406(6):39-42.
[12]KENNEDY J, EBERHART R C. Swarm intelligence[M]. San Francisco: Morgan K aufmann Publishers,2001.
[13]高海兵,高 亮,周 驰,等. 基于粒子群优化的神经网络训练算法研究[J] . 电子学报, 2004, 32(9): 1572-1574.
GAO Haibing, GAO Liang, ZHOU Chi,et al. Particle swarm optimization based algori thm for neural network learning[J].Acta Electronic Sinica,2004,32(9);1572-15 74.
[14]RUMLHART D E, HINTON G E, WILLIAMS R J. Learning representations by back propagating errors. Nature, 1986, 323(11):456-466.
[15]REED R. Pruning algorithms—a survey[J]. IEEE Transactions on Neur al Net works, 1993(4): 740-747.
[16]JACQUES RIGET, JAKOB VESTERSTR.A Diversityguided Particle Swarm Op timiz erthe ARPSO[A]. Development and Practice of Artificial Intelligence Techniqu es[C]. Durban, South Africa, 1999,41-45.
[17]KENNEDY J, SPEARS W. Matching algorithms to problems: an experimental tes t of the particle swarm and some genetic algorithms on the multimode problem gen erator[A]. In Proceedings of IEEE International Conference on Evolutionary Com putation[C]. Anchorage, Alaska, USA, 1998.
[18]马永开, 唐小我, 杨桂元. 非负权重最优组合预测方法的基本理论研究[J]. 运筹与管理, 1997, 6(2): 1-8.
MA Yongkai, TANG Xiaowo.YANG Guiyuan. A study on basic theory of the optimal com binated prediction method of non negative weights[J].Operations Research and M anagement Science, 1997,6(2):1-8.
[19]马振华. 运筹学与最优化理论[M]. 北京:清华大学出版社,1998.
[20]VAUTARD. SSA: a toolkit for noisy chaotic signals [J]. Physica D, 1992, 58: 95-126.
[21]魏凤英, 曹鸿兴. 长期预测的数学模型及应用[M].北京:气象出版社, 1990.
[22]王惠文. 偏最小二乘回归方法及其应用[M].北京:国防工业出版社,1999. 
[23]金 龙.神经网络气象预报建模理论方法与应用[M]. 北京: 气象出版社, 2004.

相似文献/References:

[1]刘三阳,张晓伟.混合差分变异策略[J].智能系统学报,2008,3(06):487.
 LIU San-yang,ZHANG Xiao-wei.A hybrid strategy for differential variation[J].CAAI Transactions on Intelligent Systems,2008,3(02):487.
[2]吴加明,吴一全.基于Tent映射CPSO和车牌纹理特征的车牌定位[J].智能系统学报,2011,6(04):333.
 WU Jiaming,WU Yiquan.License plate location based on texture features and tent chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2011,6(02):333.
[3]李秀英,李桂英,毛琳,等.采用改进粒子群算法的非线性大时滞系统无模型控制[J].智能系统学报,2013,8(03):254.
 LI Xiuying,LI guiying,MAO Lin,et al.Model-free control method for a nonlinear system with large time-delay based on IPSO[J].CAAI Transactions on Intelligent Systems,2013,8(02):254.
[4]程磊,周明达,吴怀宇,等.无线传感器环境下粒子群优化的多机器人协同定位研究[J].智能系统学报,2015,10(01):138.[doi:10.3969/j.issn.1673-4785.201310067]
 CHENG Lei,ZHOU Mingda,WU Huaiyu,et al.Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor[J].CAAI Transactions on Intelligent Systems,2015,10(02):138.[doi:10.3969/j.issn.1673-4785.201310067]
[5]李小为,胡立坤,王琥.速度约束下PSO的六自由度机械臂时间最优轨迹规划[J].智能系统学报,2015,10(03):393.[doi:10.3969/j.issn.1673-4785.201404035]
 LI Xiaowei,HU Likun,WANG Hu.PSO-based time optimal trajectory planning for six degrees of freedom robot manipulators with speed constraints[J].CAAI Transactions on Intelligent Systems,2015,10(02):393.[doi:10.3969/j.issn.1673-4785.201404035]
[6]滕旭阳,董红斌,孙静.面向特征选择问题的协同演化方法[J].智能系统学报,2017,12(01):24.[doi:10.11992/tis.201611029]
 TENG Xuyang,DONG Hongbin,SUN Jing.Co-evolutionary algorithm for feature selection[J].CAAI Transactions on Intelligent Systems,2017,12(02):24.[doi:10.11992/tis.201611029]
[7]王文彬,秦小林,张力戈,等.基于滚动时域的无人机动态航迹规划[J].智能系统学报,2018,13(04):524.[doi:10.11992/tis.201708031]
 WANG Wenbin,QIN Xiaolin,ZHANG Lige,et al.Dynamic UAV trajectory planning based on receding horizon[J].CAAI Transactions on Intelligent Systems,2018,13(02):524.[doi:10.11992/tis.201708031]
[8]于本成,丁世飞.缺失数据的混合式重建方法[J].智能系统学报,2019,14(5):947.[doi:10.11992/tis.201807037]
 YU Bencheng,DING Shifei.Hybrid reconstruction method for missing data[J].CAAI Transactions on Intelligent Systems,2019,14(02):947.[doi:10.11992/tis.201807037]

备注/Memo

备注/Memo:
收稿日期:2006-04-28.
基金项目:广西省教育厅资助项目(200508234)
作者简介:
吴建生,男,1974年生,硕士,讲师,主要研究方向为神经网络应用及智能优化算法研究
.E-mail: wjsh2002168@163.com.
更新日期/Last Update: 2009-05-05