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1通用回归神经网络在声呐目标分类中的应用BURCUERKMENTULAYYILDMM摘要应用有源声呐在浅水区水下水雷的远距离探测从而维护重要海港和海岸线区域的安全性是必需、重要的科目。人类专家从事的从海底上的水雷和岩石反馈有源声呐进行的工作通常是十分困难而且工作量非常庞大。由于它的自适应和并行处理能力，神经网络分类器已经被广泛应用在复杂声呐信号的分类中。此文，通用回归神经网络应用于解决水下目标分类的问题，作为特征抽取的主元分析已经被建立起来提高分类性能。接受器工作特性分析在神经分类器上用来评估诊断程序的灵敏度和比功率上得以应用1引言由于海洋环境的复杂性，以声呐信号为基础的水下信号的自动化识别的分类是一种复杂的问题。人类专家进行的鉴别工作通常不是一种简单的任务而且有十分庞大的工作量。具有自适应和计算优势的神经网络的出现是十分适合有源声呐的分类。GORMAN和SEJNOWSKI1所作的开创性论文有可能是记载神经网络在该领域应用的首篇论文。他们用简单特性的频谱作为神经网络分类器的输入去从定位在海洋底部沙层上柱状形的岩石中区分出水雷。在这些开创性的论文后，人们已经对利用网络进行声呐目标的自动识别产生了浓厚的兴趣。多层感知器分类器17、径向基函数网络3、8、通用回归神经网络9和随机性神经网络3、有效的前馈性神经网络在文献科研中已经广泛的应用在识别声呐信号里。特征抽取是进行复杂信号例如视觉、语音鉴别或者在声呐反馈里目标探测中被提及的问题的分类的重要预处理步骤。这些问题的输入维数对于分类来说变成不利因素，如主元分析和神经鉴别分析。特征抽取技术把高因次信号降低为在特征空间里保存最有利用价值和相关联信息的低因次特征置位。在10、11中，一些特征提取技术应用在预先给予的声呐信号中。在这篇论文中，通用回归神经网络已经被应用于鉴别在海洋底部沙层上的一个水雷和柱状形岩石两个不同目标的反馈声呐。这里重点不仅是分类器本身而且在于应用主元分析的特征抽取技术的方法，可以改善分类器的性能。接受器工作特性分析是一种在声呐研究中测试诊断性能的既定的方法。22关于GORMAN和SEJNOWSKI的数据GORMAN和SEJNOWSKI1、2，采用的最初声呐数据。这些数据组是从加里福尼亚大学机械研究数据库采集中心取得的。这些数据包含从两个来源一个金属柱面和相似形的岩石中收集来的声呐反馈，两个目标放在同一个沙层平面上，以不同的视界角向它们发射声呐线性固频脉冲，从而产生变差的数据。在GSEJNOWSKITJ,1988LEARNEDCLASSIFICATIONOFSONARTARGETSUSINGAMASSIVELYPARALLELNETWORKIEEETRANSACTIONONACOUSTICS,SPECH,ANDSIGNALPROCESSING,VOL36,NO7,PP113511402GORMANRPSEJNOWSKITJ,1988ANALYSISOFHIDDENUNITSINALAYEREDNETWORKTRAINEDTOCLASSIFYSONARTARGETSNEURALNETWOEKS,VOL1,NO1,PP75893CHEN,CH1992NEURALNETWOEKSFORACTIVESONARCLASSIFICATIONPATTERNRECOGCEEDINGS,VOLIICONFERENCEBPATTERNRECOGNITIONMETHODOLOGYANDSYSTEMS,PROCEEDINGS,11THIAPRINTERNATIONALCONFERENEON,PP4384404DIEP,DJOHANNET,ABONNEFOY,PHARROY,FLOISEAU,P,CLASSIFICATIONOFSONARDATAFORAMOBILEROBOTUSINGNEARALNETWORK1998INTELLIGENCEANDSYSTEMS,PROCEEDINGS,IEEEINTERNATIONALJOINTSYMPOSIAON,PP2572605HALEY,TB,1990APPLYINGNEARALNETWORKTOAUTOMATICACTIVCLASSIFICATIONPATTERNRECOGNITION,PROCEEDINGS,10THINTERNATIONALCONFERENEON,VOL2,PP41446SHAZEER,DJBELLO,MG,MINEHUNTINGWITHMULTILAYERPERCEPTRONS1991NEURALNETWORKFOROCEANENGINEERING,1991,IEECONFERENCEON,PP57687YUWANGJINGEIHAWAEY,FAMULTILAYEREDANNARCHITECTUREFORUNDERWATERTARGETTRACKING1994ELECTRICALANDCOMPUTERENGINEERING,CONFERENCE6PROCEEDINGS1994CANADIANCONFERENCEON,VOL2PP7857888YEGNANARAY,BCHOUHAN,HMCHANDRASEKHAR,C,SONARTARGETRECOGINTINGUSINGRADIALBASSSFUNCTIONGNETWORKS1992SINGAPOREICCSISITA92COMMUNICATIONSONTHEMOVE,VOL1,PP3953999KAPANOGLUB,YILDMMT,GENERALIZEDREGRESSIONNEURALNETWOEKSRORUNDERWATERTARGETCLASSIFICATIONNEUCEE20042NDINTERNATIONALSYMPOSIAONELECTRICALANDCOMPUTERENGINEERING,NICOSIA,NORTHCYPRUS,PP22322510SOARESFILHO,WMANOELDESEIXAS,JPERIRACALOBA,LPRINCIPALCOMPONEFNTANALYSISFORCLASSIFYINGPASSIVESONARSIGALS2001CIRCUITSANDSYSTEMS,ISCAS2001THE2001IEEEINTERNATIONALSYMPOSIAON,VOL3,PP59259511LARKIN,MJOPTIMALFEATUREEXTRACTIONTECHNIQUESTOIMPROVECLASSIFICATIONPERFORMANCE,WITHAPPLICATIONTOSONARSIGNALS1997NEURALNETWORKSFORSIGNALPROCESSINGVIIPROCEEDINGSOFTHE1997IEEEWORKSHOP,PP647112WOODS,KSBOWYER,KW,GENERATINGROCCURVESFORARTIFICIALNEURALNETWORKS1994COMPUTREBASEDMEDICALSYSTEMS,PROCEEDINGS1994IEEESEVENTHSYMPOSIUMON,PP20120613SPECHTDF,1991AGENERALIZEDREGRESSIONNEURALNETWOEKSIEEETRANSATIONSONNEURALNETWORKSVOL6,PP56857614SONGWSHAOWEIX,ROBUSTPCABASEDONNEURALNETWORKS19970PROCEEDINGSOFTHE36THCONFERENCONDECISIONTHATIS,ASTHETRAINGSETSIZEBECOMESLARGE,THEESTIMATIONERRORAPPROACHESZEROWITHONLYMILDRESTRICTIONSONTHEFUNCTIONTHEGRNNISUSEDFORESTIMATIONOFCONTINUOUSVARIABLES,ASINSTANDARREGRESSIONTECHNIQUESITISRELATEDTOTHERADIALBASISFUNCTIONNETWORKANDISBASEDONASTANDARDSTATISTICALTECHNIQUECALLEDKERNELREGRESSIONGRNNARCHITECTUREISSHOWINFIGURE1THEDETAILINFORMATIONABOUTGRNNARCHITECTUREANDSOMEMATHEMATICALCALCULATIONSCANBEEXAMINEDINTHESPECHTPAPER13X12X3X4FIG1GRNNARCHITECTURE4TRADITIONALPRINCIPALCOMPONENTANALYSISPRINCIPALCOMPONENTANALYSISISAMULTIVARIABLESTSTISTICALANALYSISTECHNIQUEOFDATACOMPRESSIONANDFEATUREEXTRACTIONENVISAGINGPRACTICALAPPLICATIONSTHATREQUIREONLINEOPERATIONSFORTHECLASSIFIER,ANATTRACTIVEAPPROACHFORTHECLASSIFIERDESIGNISTOREDUCETHEDIMENSIONALITYOFTHENDIMENSIONALINPUTSPACEBYPROJECTINGINPUTDATAONTOAREDUCEDANUMBEROFMDIRETIONSMNTHATCANFACILATATETHECLASSIFICATIONTASKTHEPCADESCRIBESTHEORIGINALDATASPACEINABASEOFEIGENVECTORSCOMPUTEDFROMPROCESSCOVARIANCEMATRIXTHECORRESPONDINGEIGENVALUESACCOUNTFORTHEENERGYOFTHEPROCESSINTHEEIGENVECTORDIRECTIONSCONSIDERINGDATAPROJECTIONRESTRICTEDTOTHEMEIGENVECTORSWITHHIGHESTEIGENVALUES,ANEFFECTIVEREDUCETIONON10DIMENSIONALALITYOFORIGINALDATAINPUTSPACECANBEACHIEVED,WITHMINIMUMINFORMATIONLOSS10THEDETAILCALCULATIONSABOUTPCACANBEFOUNDIN145SIMULATIONRESULTSTHEAIMOFTHISSTUDYISTOEMPLOYGENERALREGRESSIONNEURALNETWORKSTOCLASSIFYSONARRETURNSTOIMPROVECLASSIFICATIONPERFORMANCEANDSIMPLIFYNETWORKCOMPLEXITY,PCAISUSEDASFEATUREEXTRACTIONMETHODCLASSIFICATIONWASPERFORMEDBYUSINGMATLAB60ANDNEURALNETWORKTOOLBOXTHEDATASETCONSISTSOF208RETURNS111CYLINDERSHAPEDMINERETURNSAND97ROCKRETURNSTHEENTIREDATASETWASSPLITINTORANDOMLYTRAINANDTESTSETS103SAMPLESFORTRAININGFILE,105SAMPLESFORTESTFILETHENETWORKUSEDHEREWASBASEDONAFULLYCONNEDCTEDFEEDFORWARDNEUNALNETWORKCOMPOSEDOF60INPUTNODESANDANOUTPUTNODEINTHEFIRSTSTEPOFTHISSTUDY,GRNNWITHOUTUSINGFEATUREEXTRACTIONMETHODHASBEENAPPLIEDSONARDATAPERFORMANCEOFGRNNISAFFECTEDBYTHECHOICEOFSPREADPARAMETER,THESPREADVALUEWASOPTIMALLYFOUNDAS006THEACHIEVEDPERFORMANCEPARAMETERBYGRNNCLASSIFIER,WITHOUTUSINGPCA,ISGIVENINTABLE1TABLE1THEPERCENTAGESOFCORRECTCLASSIFICATIONFORGRNNWITHOUTSUINGPCACLASSESOVERALLOFCORRECTOFCORRECTOFCORRECT“MINE”ESTIMATION“ROCK”ESTIMATIONCLASSIFICATIONTRAINTESTTRAINTESTTRAINTEST100920610090481009142INTHESECONDSTEP,PCAHASBEENAPPLIEDTOTHEDATASETWHICHHAS60DIMENSIONALINPUTSPACETHEMAXIMUNACCURACYONTHETESTRESULTSISACHIEVEDWHEN60DIMENSIONALINPUTSPOACEISREDUCEDTO20DIMENSIONALINPUTSPACEPCAPREPROCESSINGTABLE2SHOWSTHECLASSIFICATIONPERFORMANCESOFTHEGRNNCLASSIFIERFOYTHESAMESPREADVALUETABLE2THEPERCENTAGESOFCORRECTCLASSIFICATIONFORGRNNWITHSUINGPCACLASSESOVERALLOFCORRECTOFCORRECTOFCORRECT“MINE”ESTIMATION“ROCK”ESTIMATIONCLASSIFICATIONTRAINTESTTRAINTESTTRAINTEST100936510092851009333COMPARISONTHERESULTSOFTHETABLE1ANDTABLE2DEMONSTRATESTHATCLASSIFICATIONPERFORMANCEOFGRNNCLASSIFIERCANBEIMPROVED11SUINGPCAFUETHMORE,THELOWCOMPUTATIONALCOMPLEXITYACHIEVEDBYPCAPROVIDESTHEGRNNCLASSIFIERFORONLINEOPERATIONSFINALLY,ROCANALYSISHASBEENAPPLIEDTOTESTRESULTSOFGRNNCLASSIFIERTHEEVALUATIONCRITERIAOFROCANALRSISINSONARDETECTIONINDICATETHETRADEOFFOFPROBABILITYOFDETECTIONVERSUSPROBABILITYOFFASLEDETECTIONSENSITIVITYANDSPECIFICITYARETHEBASICEXPRESSIONSEQ1ANDEQ2FORTHEDIAGNOSTICTESTINTERPRETATIONOFTHEROCANALYSISTABLE3ISEMPLOYEDFORTHESECALCULATIONSTABLE3LABELEDWITHCLASSIFICATIONRESULTSONTHELEFTSIDEANDMINEABSENT/PRESENTSTATUSONTHETOPTABLE3DIAGNOSTICTESTINTERPRETATIONTABLETP59FP3FN4TN39NUMBEROFTRUEPOSITIVESENSITIVITY09365NUMBEROFTRUEPOSITIVENUMBEROFFALSENEGATIVESNUMBEROFTRUENEGATIVESSPECIFICITY09285NUMBEROFTRUENEGATIVESNUMBEROFFALSENEGATIVES6CONCLUSIONINTHISPAPER,AGRNNHASBEENUSEDTOCLASSIFYUNDERWATERTARGETCOLLECTEDFROMTWOSOURCESAMETALCYLINDERANDSIMILARLYSHAPEDROCKTHEEMPHASISINHEREISNOTCLASSIFIERITSELFBUTALSOTHEPROCESSOFTHEFEATUREEXTRACTIONTECHNIQUE,USEDPCA,IMPROVESCLASSIFIERPERFORMANCETHELOWCOMPUTATIONALCOMPLEXITYACHIEVEDBYPCAHELPSDESIGNINGCOMPACTNEURALCLASSIFIER,WHICHISATTRACTIVEFORONLINEOPERATIONSANDHARDWAREREALIZATIONSWHENTHEPERFORMANCEOFGRNNUSINGWITHPCAARECOMPAREDWITHTHEPREVIOUSSTUDIESINTERMSOFSUCCESSFULCLASSIFICATIONRATES,THISRESULTISBETTERTHANMINEPRESENTROCKPRESENTTHERESULTOFCLASSIFIER“MINE”TRUEPOSITIVESTPFASLEPOSITIVESFPTHERESULTOFCLASSIFIER“ROCK”FASLEPOSITIVESFPTRUEPOSITIVESTP12THOSEWITHTHEACCURACIESOFMAXIMUM9041ANDROCANALYSISRESULTSWHICHISCLOSETO1SENSITIVITYANDSPECIFICITYSHOWSTHERELIABILITYOFTHISCLASSIFICATIONREFERNCES15GORMANRPSEJNOWSKITJ,1988LEARNEDCLASSIFICATIONOFSONARTARGETSUSINGAMASSIVELYPARALLELNETWORKIEEETRANSACTIONONACOUSTICS,SPECH,ANDSIGNALPROCESSING,VOL36,NO7,PP1135114016GORMANRPSEJNOWSKITJ,1988ANALYSISOFHIDDENUNITSINALAYEREDNETWORKTRAINEDTOCLASSIFYSONARTARGETSNEURALNETWOEKS,VOL1,NO1,PP758917CHEN,CH1992NEURALNETWOEKSFORACTIVESONARCLASSIFICATIONPATTERNRECOGCEEDINGS,VOLIICONFERENCEBPATTERNRECOGNITIONMETHODOLOGYANDSYSTEMS,PROCEEDINGS,11THIAPRINTERNATIONALCONFERENEON,PP43844018DIEP,DJOHANNET,ABONNEFOY,PHARROY,FLOISEAU,P,CLASSIFICATIONOFSONARDATAFORAMOBILEROBOTUSINGNEARALNETWORK1998INTELLIGENCEANDSYSTEMS,PROCEEDINGS,IEEEINTERNATIONALJOINTSYMPOSIAON,PP25726019HALEY,TB,1990APPLYINGNEARALNETWORKTOAUTOMATICACTIVCLASSIFICATIONPATTERNRECOGNITION,PROCEEDINGS,10THINTERNATIONALCONFERENEON,VOL2,PP414420SHAZEER,DJBELLO,MG,MINEHUNTINGWITHMULTILAYERPERCEPTRONS1991NEURALNETWORKFOROCEANENGINEERING,1991,IEECONFERENCEON,PP576821YUWANGJINGEIHAWAEY,FAMULTILAYEREDANNARCHITECTUREFORUNDERWATERTARGETTRACKING1994ELECTRICALANDCOMPUTERENGINEERING,CONFERENCEPROCEEDINGS1994CANADIANCONFERENCEON,VOL2PP78578822YEGNANARAY,BCHOUHAN,HMCHANDRASEKHAR,C,SONARTARGETRECOGINTINGUSINGRADIALBASSSFUNCTIONGNETWORKS1992SINGAPOREI