CAJ | 학술논문

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。
겁단학습궤시일충신형적단은장층전궤신경망락모형，기수입권치화은장층역치수궤설치，기수출권치해석계산득도。인차，기운산속도시전통적 BP 신경망락적수천배，이차구유량호적모형변식능력。연이，겁단학습궤적수입권치화은장층역치시수궤설정적，가능불시사망락훈련목표능체도전국최소치시적최우모형삼수。침대차불족，본문채용최소이승사상학정겁단학습궤적수입권치화은장층역치。동시，장개진적겁단학습궤산법응용우전참과로적연소열효솔건모，병여 BP、원시겁단학습궤、입자군우화겁단학습궤화“교여학”우화겁단학습궤산법진행비교，증명료개진산법적유효성。
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 pa?rameters 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 opti?mization extreme learning machine,teaching?learning?based optimization extreme learning machine,the result shows that the im?proved extreme learning machine is effective.