全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

改进式混合增量极限学习机算法

DOI: 10.13195/j.kzyjc.2014.1314, PP. 1981-1986

Keywords: 极限学习机,增量学习算法,Delta,检验,混沌优化算法,增量型极限学习机

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对增量型极限学习机(I-ELM)中存在大量降低学习效率及准确性的冗余节点的问题,提出一种基于Delta检验(DT)和混沌优化算法(COA)的改进式增量型核极限学习算法.利用COA的全局搜索能力对I-ELM中的隐含层节点参数进行寻优,结合DT算法检验模型输出误差,确定有效的隐含层节点数量,从而降低网络复杂程度,提高算法的学习效率;加入核函数可增强网络的在线预测能力.仿真结果表明,所提出的DCI-ELMK算法具有较好的预测精度和泛化能力,网络结构更为紧凑.

References

[1]  Huang G B, Zhu Q, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[2]  Zong W, Huang G B. Face recognition based on extreme learning machine[J]. Neurocomputing, 2011, 74(16): 2541-2551.
[3]  颜七笙, 王士同, 张延飞, 等. 基于经验模式分解和极限学习机的铀资源价格预测方法[J]. 控制与决策, 2014, 29(7): 1187-1192.
[4]  (Yan Q S, Wang S T, Zhang Y F, et al. Uranium resource price prediction based on empirical mode decomposition and extreme learning machine[J]. Control and Decision, 2014, 29(7): 1187-1192.)
[5]  Huang G B, Li M B, Chen L, et al. Incremental extreme learning machine with fully complex hidden nodes[J]. Neurocomputing, 2008, 71: 576-583.
[6]  田慧欣, 王安娜. 基于增量学习思想的改进AdaBoost 建模方法[J]. 控制与决策, 2012, 27(9): 1433-1436.
[7]  (Tian H X, Wang A N. Improved adaboost modeling method based on incremental learning[J]. Control and Decision, 2010, 27(9): 1433-1436.)
[8]  Huang G B, Chen L. Enhanced random search based incremental extreme learning machine[J]. Neurocomputing, 2008, 71: 3460-3468.
[9]  Huang G B, Chen L. Convex incremental extreme learning machine[J]. Neurocomputing, 2007, 70: 3056-3062.
[10]  Wang W, Zhang R. Improved convex incremental extreme learning machine based on enhanced random search[J]. Unifying Electrical Engineering and Electronics Engineering, 2014, 238: 2033-2040.
[11]  Zhang R, Lan Y, Huang G B, et al. Universal approximation of extreme learning machine with adaptive growth of hidden nodes[J]. IEEE Trans on Neural Networks and Learning Systems, 2012, 23: 365-371.
[12]  李凡军, 乔俊飞, 韩红桂. 网络结构增长的极端学习机算法[J]. 控制理论与应用, 2014, 31(5): 638-643.
[13]  (Li F J, Qiao J F, Han H G. Incremental constructive extreme learning machine[J]. Control Theory & Applications, 2014, 31(5): 638-643.)
[14]  Guo L, Hao J H, Liu M. An incremental extreme learning machine for online sequential learning problems[J]. Neurocomputing, 2014, 128: 50-58.
[15]  Yang Y M, Wang Y N, Yuan X F, et al. Hybrid chaos optimization algorithm with artificial emotion[J]. Applied Mathematics and Computation, 2012, 218(11): 6585-6591.
[16]  Yang Y M, Wang Y N, Yuan X F. Parallel chaos search based incremental extreme learning machine[J]. Neural Processing Letters, 2013, 37: 277-301.
[17]  Jones A J. New tools in non-linear modeling and prediction[J]. Computational Management Science, 2004, 1(2): 109-149.
[18]  Stef′??nsson A, Koncar N, Jones A J. A note on the gamma test[J]. Nerual Comput, 1997, 5(3): 131-133.
[19]  Yu Q, Heeswijk M V, Miche Y, et al. Ensemble delta test-extreme learning machine(DT-ELM) for regression[J]. Neurocomputing, 2014, 129: 153-158.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133