极端学习机算法的改进及应用研究
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摘要
极端学习机是一种新型的单隐藏层前馈神经网络模型,其输入权值和隐藏层阈值随机设置,其输出权值解析计算得到。因此,其运算速度是传统的BP神经网络的数千倍,而且具有良好的模型辨识能力。然而,极端学习机的输入权值和隐藏层阈值是随机设定的,可能不是使网络训练目标能达到全局最小值时的最优模型参数。针对此不足,本文采用最小二乘思想确定极端学习机的输入权值和隐藏层阈值。同时,将改进的极端学习机算法应用于电站锅炉的燃烧热效率建模,并与BP、原始极端学习机、粒子群优化极端学习机和"教与学"优化极端学习机算法进行比较,证明了改进算法的有效性。
Extreme learning machine is a novel single hidden layer feed-forward neural network model,whose input weights and the bias of hidden nodes are generated randomly. And its output weights are computed analytically. Consequently,the extreme learning machine owns extremely fast speed and good identification ability,which is faster than conventional BP neural network thousands times. However,the stochastic input weights and the bias of the extreme learning machine are not the best model parameters possibly when the objective function gets the global minimum value. Therefore,the least square method is adopted to seek the appropriate parameters of extreme learning machine. The improved extreme learning machine is applied to build the combustion thermal efficiency model of the plant boiler. Compared with other algorithms,such as BP,conventional extreme learning machine,particle swarm optimization extreme learning machine,teaching-learning-based optimization extreme learning machine,the result shows that the improved extreme learning machine is effective.
Extreme learning machine is a novel single hidden layer feed-forward neural network model,whose input weights and the bias of hidden nodes are generated randomly. And its output weights are computed analytically. Consequently,the extreme learning machine owns extremely fast speed and good identification ability,which is faster than conventional BP neural network thousands times. However,the stochastic input weights and the bias of the extreme learning machine are not the best model parameters possibly when the objective function gets the global minimum value. Therefore,the least square method is adopted to seek the appropriate parameters of extreme learning machine. The improved extreme learning machine is applied to build the combustion thermal efficiency model of the plant boiler. Compared with other algorithms,such as BP,conventional extreme learning machine,particle swarm optimization extreme learning machine,teaching-learning-based optimization extreme learning machine,the result shows that the improved extreme learning machine is effective.
引文
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[4]张弦,王宏力.基于贯序正则极端学习机的时间序列预测及其应用[J].航空学报,2011,32(7):1302-1308.
[5]Huang G B,Wang D H,Lan Y.Extreme learning machines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122.
[6]Li G,Niu P.An enhanced extreme learning machine based on ridge regression for regression[J].Neural Computing and Applications,2013,22(3/4):803-810.
[7]Liang N Y,Huang G B,Saratchandran P,et al.A fast and accurate online sequential learning algorithm for feedforward networks[J].IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[8]Zhu Q Y,Qin A K,Suganthan P N,et al.Evolutionary extreme learning machine[J].Pattern recognition,2005,38(10):1759-1763.
[9]Matias T,Souza F,Araújo R,et al.Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine[J].Neurocomputing,2014,129:428-436.
[10]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.
[11]Li G,Niu P,Ma Y,et al.Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency[J].Knowledge-Based Systems,2014,67:278-289.
[12]Li G,Niu P,Liu C,et al.Enhanced combination modeling method for combustion efficiency in coal-fired boilers[J].Applied Soft Computing,2012,12(10):3132-3140.
[13]Rao R V,Savsani V J,Vakharia D P.Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J].Computer-Aided Design,2011,43(3):303-315.
[14]Rao R V,Savsani V J,Vakharia D P.Teaching-learning-based optimization:an optimization method for continuous non-linear large scale problems[J].Information Sciences,2012,183(1):1-15.
[2]Li G,Niu P,Duan X,et al.Fast learning network:a novel artificial neural network with a fast learning speed[J].Neural Computing and Applications,2014,24(7/8):1683-1695.
[3]张弦,王宏力.限定记忆极端学习机及其应用[J].控制与决策,2012,27(8):1206-1210.
[4]张弦,王宏力.基于贯序正则极端学习机的时间序列预测及其应用[J].航空学报,2011,32(7):1302-1308.
[5]Huang G B,Wang D H,Lan Y.Extreme learning machines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122.
[6]Li G,Niu P.An enhanced extreme learning machine based on ridge regression for regression[J].Neural Computing and Applications,2013,22(3/4):803-810.
[7]Liang N Y,Huang G B,Saratchandran P,et al.A fast and accurate online sequential learning algorithm for feedforward networks[J].IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[8]Zhu Q Y,Qin A K,Suganthan P N,et al.Evolutionary extreme learning machine[J].Pattern recognition,2005,38(10):1759-1763.
[9]Matias T,Souza F,Araújo R,et al.Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine[J].Neurocomputing,2014,129:428-436.
[10]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.
[11]Li G,Niu P,Ma Y,et al.Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency[J].Knowledge-Based Systems,2014,67:278-289.
[12]Li G,Niu P,Liu C,et al.Enhanced combination modeling method for combustion efficiency in coal-fired boilers[J].Applied Soft Computing,2012,12(10):3132-3140.
[13]Rao R V,Savsani V J,Vakharia D P.Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J].Computer-Aided Design,2011,43(3):303-315.
[14]Rao R V,Savsani V J,Vakharia D P.Teaching-learning-based optimization:an optimization method for continuous non-linear large scale problems[J].Information Sciences,2012,183(1):1-15.