基于改进bp神经网络的围岩自稳能力评估模型

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1、基于改进BP神经网络的围岩自稳能力评估模型摘要:指挥防护工程是国家防护工程体系的重要组成部分。为提高其建设水平，采用改进的前馈(bp)神经网络，对指挥防护工程围岩自稳能力进行评估。结合指挥防护工程围岩的特点，设计评估网络拓扑结构。针对bp网络原始模型的缺陷改进，引入动量项、自适应调节学习率、陡度因子、可变隐层节点等，并采用遗传算法（ga）寻找最优的初始权值和阈值。最后结合实例对算法进行验证。结果表明，该模型科学可靠，具有较好的工程应用价值。关键词:前馈神经网络；遗传算法；评估；围岩；自稳能力；指挥防护工程self.stabilityevaluationmodelofsurr

2、oundingrockbasedonimprovedbpneuralnetworkwangduo.dian1,2*,qiuguo.qing1,daiting.ting3,wangyue11.engineeringinstituteofcorpsofengineers，plauniversityofscienceandtechnology，nanjingjiangsu210007，china;2.unit66081ofpla,huailaihebei050083,china;3.chinasatellitemaritimetrackingandcont

3、rollingdepartment,jiangyinjiangsu214431,chinaabstract:commandprotectionengineeringistheimportantcomponentofnationalprotectionengineeringsystem.toraisethelevelofconstructionofcommandprotectionengineering,thebackpropagation(bp)neuralnetworkisimprovedtogiveresearchonself-stabilityevaluationofi

4、ts’surroundingrock.firstly,thenetworktopologyisdevised，basedonthepointofsurroundingrock.secondly,themodelisimprovedaccordingtoitsdisadvantages,byintroducingthemomentum,self-adaptiveadjustinglearnrate,variablehiddennodesandsteepfactor,furthermore,geneticalgorithm(ga)isimportedtoseekitsbestin

5、itialweightandthresholdvalue.finally,beusedtoacertaincommandprotectionengineering，themodelisprovedtobecredibleandprecise.commandprotectionengineeringistheimportantcomponentofnationalprotectionengineeringsystem.toraisethelevelofconstructionofcommandprotectionengineering,thebackpropagation(bp

6、)neuralnetworkwasimprovedtogiveresearchonself.stabilityevaluationofitssurroundingrock.firstly,thenetworktopologywasdevised，basedonthecharacteristicsofsurroundingrock.secondly,themodelwasimprovedaccordingtoitsdisadvantages,byintroducingthemomentum,self.adaptiveadjustinglearnrate,variablehidd

7、ennodesandsteepfactor;furthermore,geneticalgorithm(ga)wasimportedtoseekitsbestinitialweightandthresholdvalue.finally,aninstancewasgiventovalidatethealgorithm.theresultsshowthatthemodelisscientificallyreliableandofbettervalueinengineering.keywords:backpr