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基于粒子群优化的深度神经网络分类算法董晴;宋威【摘要】针对神经网络分类算法中节点函数不可导,分类精度不够高等问题,提出了一种基于粒子群优化(PSO)算法的深度神经网络分类算法.使用深度学习中的自动编码机,结合PSO算法优化权值,利用自动编码机对输入样本数据进行编解码,为提高网络分类精度,以编码机本身的误差函数和Softmax分类器的代价函数加权求和共同作为PSO算法的评价函数,使编码后的数据更加适应分类器.实验结果证明:与其他传统的神经网络相比,在邮件分类问题上,此分类算法有更高的分类精度.%Aiming at problem that classification precision of neural network algorithm is not very high and node function doesn't have derivate,a new classification algorithm of deep neural network based on particle swarm optimization(PSO) is e autoencoder of deep study,and combined with PSO algorithm to optimize the weight,coder and decoder for input sample data using autoencoder.In order to improve the classification precision of network,take the error function of autoencoder and cost function of softmax classifier weight sum as evaluation function of PSO algorithm in common,making coded data more adapter to the classifier.The experimental results show that compared with other traditional neural network,the classification algorithm has higher classification precision on Email classification.【期刊名称】《传感器与微系统》【年(卷),期】2017(036)009【总页数】5页(P143-146,150)【关键词】深度神经网络;自动编码机;粒子群优化算法;分类【作者】董晴;宋威【作者单位】江南大学物联网工程学院,江苏无锡214122;江南大学物联网工程学院,江苏无锡214122【正文语种】中文【中图分类】TP183近年来，神经网络的研究一直受到学者们的关注，如感知机[1]，反向传播(back propogation,BP)神经网络[2]，径向基函数(radial basis function,RBF)神经网络及其各种改进算法[3～5]等。
