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一种新的混合智能极限学习机

DOI: 10.13195/j.kzyjc.2014.0321, PP. 1078-1084

Keywords: 粒子群优化算法,差分进化算法,极限学习机,混合

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Abstract:

提出一种基于差分进化(DE)和粒子群优化(PSO)的混合智能方法—–DEPSO算法,并通过对10个典型函数进行测试,表明DEPSO算法具有良好的寻优性能.针对单隐层前向神经网络(SLFNs)提出一种改进的学习算法-----DEPSO-ELM算法,即应用DEPSO算法优化SLFNs的隐层节点参数,采用极限学习算法(ELM)求取SLFNs的输出权值.将DEPSO-ELM算法应用于6个典型真实数据集的回归计算,并与DE-ELM、SaE-ELM算法相比,获得了更精确的计算结果.最后,将DEPSO-ELM算法应用于数控机床热误差的建模预测,获得了良好的预测效果.

References

[1]  刘德荣, 李宏亮, 王鼎. 基于数据的自学习优化控制: 研究进展与展望[J]. 自动化学报, 2013, 39(11): 1858-1870.
[2]  (Liu D R, Li H L, Wang D. Data-based self-learning optimal control: Research progress and prospects[J]. Acta Automatica Sinica, 2013, 39(11): 1858-1870.)
[3]  Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501.
[4]  Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Trans on Evolutionary Computation, 2009, 13(2): 398-417.
[5]  Cao J, Lin Z, Huang G B. Self-adaptive evolutionary extreme learning machine[J]. Neural Processing Letters, 2012, 36(3): 285-305.
[6]  Zhu Q Y, Qin A K, Suganthan P N, et al. Evolutionary extreme learning machine[J]. Pattern Recognition, 2005, 38(10): 1759-1763.
[7]  Han F, Yao H F, Ling Q H. An improved evolutionary extreme learning machine based on particle swarm optimization[J]. Neurocomputing, 2013, 116: 87-93.
[8]  Miche Y, Sorjamaa A, Bas P, et al. OP-ELM: Optimally pruned extreme learning machine[J]. IEEE Trans on Neural Networks, 2010, 21(1): 158-162.
[9]  崔文华, 刘晓冰, 王伟, 等. 混合蛙跳算法研究综述[J]. 控制与决策, 2012, 27(4): 481-486.
[10]  (CuiWH, Liu X B,WangW, et al. Survey on shuffled frog leaping algorithm[J]. Control and Decision, 2012, 27(4): 481-486.)
[11]  Storn R, Price K. Differential evolution ― Asimple and efficient heuristics for global optimization over continuous spaces[J]. J of Global Optimization, 1997, 11(4): 341-359.
[12]  辛斌, 陈杰. 粒子群优化与差分进化混合算法的综述与分类[J]. 系统科学与数学, 2012, 31(9): 1130-1150.
[13]  (Xin B, Chen J. Particle swarm optimization and differiential evolution: State of the art[J]. J of Systems Science and Mathematical Sciences, 2012, 31(9): 1130-1150.)
[14]  Kennedy J, Eberhart R C. Particle swarm optimization[C]. Proc of the IEEE Int Conf on Neural Networks. Perth, 1995: 1942-1948.
[15]  Kim P, Lee J. An integrated method of particle swarm optimization and differential evolution[J]. J of Mechanical Science and Technology, 2009, 23(2): 426-434.
[16]  Hao Z F, Guo G H, Huang H. A particle swarm optimization algorithm with differential evolution[C]. Proc of the 6th Int Conf on Machine Learning and Cybernetics. Hong Kong, 2007: 1031-1035.
[17]  Xin B, Chen J, Peng Z H, et al. An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization[J]. Science China Information Sciences, 2010, 53(5): 980-989.
[18]  Zhang C S, Ning J X, Lu S, et al. A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization[J]. Operations Research Letters, 2009, 37(2): 117-122.
[19]  Kannan S, Slochanal S M R, Subbaraj P, et al. Application of particle swarm optimization technique and its variants to generation expansion planning[J]. Electric Power Systems Research, 2004, 70(3): 203-210.
[20]  李永祥. 数控机床热误差建模新方法及其应用研究[D]. 上海: 上海交通大学机械与动力工程学院, 2007.
[21]  (Li Y X. New methods of thermal error modeling for NC machine tools and their researchment and application[D]. Shanghai: School of Mechanical & Power Engineering, Shanghai Jiaotong University, 2007.)
[22]  田慧欣, 毛志忠. 基于Bagging 的多模型钢水温度预报[J]. 控制与决策, 2009, 24(5): 687-691.
[23]  (Tian H X, Mao Z Z. Multi-model prediction of molten steel temperature based on bagging[J]. Control and Decision, 2009, 24(5): 687-691.)

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