Matlab的神經網絡工具箱入門在commandwindow中鍵入helpnnet>>helpnnetNeuralNetworkToolboxVersion7.0（R2010b）03-Aug-2010神經網絡工具箱版本7.0（R2010b）03八月，2010圖形用戶界面功能。nnstart-神經網絡啟動GUInctool-神經網絡分類工具nftool-神經網絡的擬合工具nntraintool-神經網絡的訓練工具nprtool-神經網絡模式識別工具ntstool-NFTool神經網絡時間序列的工具nntool-神經網絡工具箱的圖形用戶界面。查看-查看一個神經網絡。網絡的建立功能。cascadeforwardnet-串級，前饋神經網絡。competlayer-競爭神經層。distdelaynet-分布時滯的神經網絡。elmannet-Elman神經網絡。feedforwardnet-前饋神經網絡。fitnet-函數擬合神經網絡。layrecnet-分層遞歸神經網絡。linearlayer-線性神經層。lvqnet-學習矢量量化（LVQ）神經網絡。narnet-非線性自結合的時間序列網絡。narxnet-非線性自結合的時間序列與外部輸入網絡newgrnn-設計一個廣義回歸神經網絡。newhop-建立經常性的Hopfield網絡。newlind-設計一個線性層。newpnn-設計概率神經網絡。newrb-徑向基網絡設計。newrbe-設計一個確切的徑向基網絡。patternnet-神經網絡模式識別。感知-感知。selforgmap-自組織特征映射。timedelaynet-時滯神經網絡。利用網絡。網絡-創建一個自定義神經網絡。SIM卡-模擬一個神經網絡。初始化-初始化一個神經網絡。適應-允許一個神經網絡來適應。火車-火車的神經網絡。DISP鍵-顯示一個神經網絡的屬性。顯示-顯示的名稱和神經網絡屬性adddelay-添加延遲神經網絡的反應。closeloop-神經網絡的開放反饋轉換到關閉反饋回路。formwb-表格偏見和成單個向量的權重。getwb-將它作為一個單一向量中的所有網絡權值和偏差noloop-刪除神經網絡的開放和關閉反饋回路。開環-轉換神經網絡反饋，打開封閉的反饋循環。removedelay-刪除延遲神經網絡的反應。separatewb-獨立的偏見和重量/偏置向量的權重。setwb-將所有與單個矢量網絡權值和偏差。Simulink的支持。gensim-生成Simulink模塊來模擬神經網絡。setsiminit-集神經網絡的Simulink模塊的初始條件getsiminit-獲取神經網絡Simulink模塊的初始條件神經元-神經網絡Simulink的模塊庫。培訓職能。trainb-批具有重量與偏見學習規則的培訓。trainbfg-的BFGS擬牛頓倒傳遞。trainbr-貝葉斯規則的BP算法。trainbu-與重量與偏見一批無監督學習規則的培訓。trainbuwb-與體重無監督學習規則與偏見一批培訓。trainc-循環順序重量/偏見的培訓。traincgb-共軛鮑威爾比爾重新啟動梯度反向傳播。traincgf-共軛弗萊徹-里夫斯更新梯度反向傳播。traincgp-共軛波拉克-Ribiere更新梯度反向傳播。traingd-梯度下降反向傳播。traingda-具有自適應LR的反向傳播梯度下降。traingdm-與動量梯度下降。traingdx-梯度下降瓦特/慣性與自適應LR的反向傳播。trainlm-采用Levenberg-馬奎德倒傳遞。trainoss-一步割線倒傳遞。trainr-隨機重量/偏見的培訓。trainrp-RPROP反向傳播。trainru-無監督隨機重量/偏見的培訓。火車-順序重量/偏見的培訓。trainscg-規模化共軛梯度BP算法。繪圖功能。plotconfusion-圖分類混淆矩陣。ploterrcorr-誤差自相關時間序列圖。ploterrhist-繪制誤差直方圖。plotfit-繪圖功能適合。plotinerrcorr-圖輸入錯誤的時間序列的互相關。plotperform-小區網絡性能。

plotregression-線性回歸情節。plotresponse-動態網絡圖的時間序列響應。plotroc-繪制受試者工作特征。plotsomhits-小區自組織圖來樣打。plotsomnc-小區自組織映射鄰居的連接。plotsomnd-小區自組織映射鄰居的距離。plotsomplanes-小區自組織映射重量的飛機。plotsompos-小區自組織映射重量立場。plotsomtop-小區自組織映射的拓撲結構。plottrainstate-情節訓練狀態值。plotwb-圖寒春重量和偏差值圖。列出其他神經網絡實現的功能。nnadapt-適應職能。nnderivati??ve-衍生功能。nndistance-距離函數。nndivision-除功能。nninitlayer-初始化層功能。nninitnetwork-初始化網絡功能。nninitweight-初始化權函數。nnlearn-學習功能。nnnetinput-凈輸入功能。nnperformance-性能的功能。nnprocess-處理功能。nnsearch-線搜索功能。nntopology-拓撲結構的功能。nntransfer-傳遞函數。nnweight-重量的功能。示威，數據集和其他資源nndemos-神經網絡工具箱的示威。nndatasets-神經網絡工具箱的數據集。nntextdemos-神經網絡設計教科書的示威。nntextbook-神經網絡設計教科書的資訊。調出圖形用戶界面如下用他給出的命令nntool（同樣是在commandwindow鍵入）調出圖形用戶界面如下■InputData:Import...New...⑥HelpQClose>>helpnnetNeuralNetworkToolbox神經網絡工具箱Version8.1(R2013b)08-Aug-20132013的8.1版Graphicaluserinterfacefunctions.圖形用戶界面函數nnstart-NeuralNetworkStartGUI神經網絡啟動圖形用戶界面GUInctool-Neuralnetworkclassificationtool神經網絡分類工具nftool-NeuralNetworkFittingTool神經網絡擬合工具nntraintool-Neuralnetworktrainingtool神經網絡訓練工具nprtool-Neuralnetworkpatternrecognitiontool神經網絡模式識別工具ntstool-NFToolNeuralNetworkTimeSeriesToolNFTool神經網絡時間序列工具nntool-NeuralNetworkToolboxgraphicaluserinterface.神經網絡工具箱圖形用戶界面view-Viewaneuralnetwork.查看一個神經網絡Networkcreationfunctions.網絡生成函數cascadeforwardnet-Cascade-forwardneuralnetwork.級聯神經網絡competlayer-Competitiveneurallayer.競爭神經層distdelaynet-Distributeddelayneuralnetwork.分布式延遲神經網絡elmannet-Elmanneuralnetwork.Elman神經網絡feedforwardnet-Feed-forwardneuralnetwork.前饋神經網絡fitnet-Functionfittingneuralnetwork.函數擬合神經網絡layrecnet-Layeredrecurrentneuralnetwork.分層遞歸神經網絡linearlayer-Linearneurallayer.線性神經層lvqnet-Learningvectorquantization(LVQ)neuralnetwork.學習向量量化(LVQ)神經網絡

narnet間序列網絡narxnet-Nonlinearauto-associativetime-seriesnetwork.非線性自動組合時-Nonlinearauto-associativetime-seriesnetworkwithexternalinput.具有外部輸入的非線性自動組合時間序列網絡newgrnn-Designageneralizedregressionneuralnetwork.設計一個廣義回歸神經網絡newhop網絡newlind-CreateaHopfieldrecurrentnetwork.創建一個Hopfield復發性-Designalinearlayer.設計一個線性層newpnnnewrbnewrbepatternnetperceptronselforgmap-Designaprobabilisticneuralnetwork.設計一個概率神經網絡-Designaradialbasisnetwork.設計一個徑向基網絡-Designanexactradialbasisnetwork.設計一個精確的徑向基網絡-Patternrecognitionneuralnetwork.模式識別神經網絡-Perceptron.-Self-organizingmap.自組織映射timedelaynet-Time-delayneuralnetwork.時間延遲神經網絡Usingnetworks.網絡使用networksiminitadapttraindispdisplay名字和屬性-Createacustomneuralnetwork.創建一個定制的神經網絡-Simulateaneuralnetwork.模擬神經網絡-Initializeaneuralnetwork.初始化一個神經網絡-Allowaneuralnetworktoadapt.神經網絡的適應-Trainaneuralnetwork.訓練一個神經網絡-Displayaneuralnetwork'sproperties.顯示一個神經網絡的屬性-Displaythenameandpropertiesofaneuralnetwork顯示一個神經網絡的adddelaycloseloop-Addadelaytoaneuralnetwork'sresponse.加一個延時到神經網絡響應-Convertneuralnetworkopenfeedbacktoclosedfeedbackloops.轉變神經網絡打開反饋到關閉反饋的回路formwbgetwb-Formbiasandweightsintosinglevector.使偏差和權重成為單一向量-Getallnetworkweightandbiasvaluesasasinglevector.獲得全部網絡權重和偏差當作單一向量noloop-Removeneuralnetworkopenandclosedfeedbackloops.移去神經網絡打開和關閉反饋回路openloop-Convertneuralnetworkclosedfeedbacktoopenfeedbackloops.轉變神經網絡關閉反饋到打開反饋的回路removedelay-Removeadelaytoaneuralnetwork'sresponse.為神經網絡反應移去一個延遲separatewb-Separatebiasesandweightsfromaweight/biasvector.從一個權重/偏差向量分離偏差和權重setwb-Setallnetworkweightandbiasvalueswithasinglevector.用一個單一向量設置全部網絡權重和偏差值Simulinksupport.仿真支持gensim-GenerateaSimulinkblocktosimulateaneuralnetwork.生成Simulink模塊來模擬神經網絡setsiminit-SetneuralnetworkSimulinkblockinitialconditions設置神經網絡Simulink模塊初始條件getsiminit-GetneuralnetworkSimulinkblockinitialconditions獲得神經網絡Simulink模塊初始條件neural-NeuralnetworkSimulinkblockset.神經網絡Simulink模塊集Trainingfunctions.訓練函數trainb-Batchtrainingwithweight&biaslearningrules.批處理具有權重和偏差學習規則的訓練trainbfg-BFGSquasi-Newtonbackpropagation.BFGS擬牛頓反向傳播trainbr-BayesianRegulationbackpropagation.貝葉斯規則的反向傳播trainbu-Unsupervisedbatchtrainingwithweight&biaslearningrules.無監管的批處理具有權重和偏差學習規則的訓練trainbuwb-Unsupervisedbatchtrainingwithweight&biaslearningrules.無監管的批處理具有權重和偏差學習規則的訓練trainc-Cyclicalorderweight/biastraining.循環順序權重和偏差訓練traincgb-ConjugategradientbackpropagationwithPowell-Bealerestarts.具有Powell-Beale重新開始的共軛梯度反向傳播traincgf-ConjugategradientbackpropagationwithFletcher-Reevesupdates.具有Fletcher-Reeves更新的共軛梯度反向傳播traincgp-ConjugategradientbackpropagationwithPolak-Ribiereupdates.具有Polak-Ribiere更新的共軛梯度Polak-Ribieretraingd-Gradientdescentbackpropagation.梯度下降反向傳播traingda-Gradientdescentwithadaptivelrbackpropagation.具有自適應LR的反向傳播梯度下降traingdm-Gradientdescentwithmomentum.具有動量的梯度下降traingdx-Gradientdescentw/momentum&adaptivelrbackpropagation.梯度下降瓦特/動量與自適應LR的反向傳播trainlm-Levenberg-Marquardtbackpropagation.Levenberg-Marquardt反向傳播trainoss-Onestepsecantbackpropagation.一步割線反向傳播trainr-Randomorderweight/biastraining.隨機權重/偏差訓練trainrp-RPROPbackpropagation.RPROP反向傳播trainru-Unsupervisedrandomorderweight/biastraining.無監管隨機權重/偏差訓練trains-Sequentialorderweight/biastraining.順序權重/偏差訓練trainscg-Scaledconjugategradientbackpropagation.規模化共軛梯度反向傳播Plottingfunctions.繪圖函數plotconfusion-Plotclassificationconfusionmatrix.圖分類混淆矩陣ploterrcorr-Plotautocorrelationoferrortimeseries.誤差自相關時間序列圖ploterrhist-Ploterrorhistogram.誤差直方圖plotfit-Plotfunctionfit.繪圖功能（函數）配合plotinerrcorr-Plotinputtoerrortimeseriescross-correlation.繪制輸入誤差時間序列互相關plotperform-Plotnetworkperformance.繪制網絡性能plotregression-Plotlinearregression.繪制線性回歸plotresponse-Plotdynamicnetworktime-seriesresponse.繪制動態網絡時間序列反應plotroc-Plotreceiveroperatingcharacteristic.繪制接收器操作特性plotsomhits-Plotself-organizingmapsamplehits.繪制自組織映射樣本采樣數plotsomnc-PlotSelf-organizingmapneighborconnections.繪制自組織映射鄰居關系plotsomnd-PlotSelf-organizingmapneighbordistances.繪制自組織映射鄰居距離plotsomplanes-Plotself-organizingmapweightplanes.繪制自組織映射權重位面plotsompos-Plotself-organizingmapweightpositions.繪制自組織映射權重位置plotsomtop-Plotself-organizingmaptopology.繪制自組織映射拓撲結構plottrainstate-Plottrainingstatevalues.繪制訓練狀態值plotwb-PlotHintondiagramsofweightandbiasvalues.繪制權重和偏差值得Hinton圖Listsofotherneuralnetworkimplementationfunctions.神經網絡其他重要函數的列表nnadapt-Adaptfunctions.適應函數adaptwb-Sequentialorderincrementaltrainingw/learningfunctions.nnderivative-Derivativefunctions.派生（衍生）函數NeuralNetworkToolboxCalculationFunctions.神經網絡工具箱計算函數Thecontentsofthisdirectorymaychangewithoutnotice.Thesefilesareimplementationfilesnotintendedfordirectuse.這個目錄的內容可以改變故沒有公告，這些文件是安裝啟用文件并非直接可應用的。nndistance-Distancefunctions.距離函數boxdist-Boxdistancefunction.Box距離函數dist-Euclideandistanceweightfunction.歐幾里得距離權值函數linkdist-Linkdistancefunction.Link距離函數mandist-Manhattandistancefunction.Manhattan距離函數nndivision-Divisionfunctions.除法函數divideblock-Partitionindicesintothreesetsusingblocksofindices.劃分指標成為三個可用指標塊集divideind-Partitionindicesintothreesetsusingspecifiedindices.劃分指標成為三個可用指定指標集divideint-Partitionindicesintothreesetsusinginterleavedindices.劃分指標成為三個可用交叉存儲指標集dividerand-Partitionindicesintothreesetsusingrandomindices.劃分指標成為三個可用隨機指標集dividetrain-Partitionindicesintotrainingsetonly.劃分指標僅成為訓練集nninitlayer-Initializelayerfunctions.初始化層函數initnw-Nguyen-Widrowlayerinitializationfunction.Nguyen-Widrow層初始化函initwb-By-weight-and-biaslayerinitializationfunction.附近（次要的）-權值和閾值層初始化函數nninitnetwork-Initializenetworkfunctions.初始化網絡函數initlay-Layer-by-layernetworkinitializationfunction.疊層網絡初始化函數nninitweight-Initializeweightfunctions.初始化權值函數Biasonlyinitializationfunctions僅閾值初始化函數initcon-Consciencebiasinitializationfunction.Conscience閾值初始化函數nnlearn-Learningfunctions.學習函數learncon-Consciencebiaslearningfunction.Conscience閾值學習函數learngd-Gradientdescentweight/biaslearningfunction.梯度下降權值/閾值學習函數learngdm-Gradientdescentw/momentumweight/biaslearningfunction.梯度下降w/動量權值/閾值學習函數learnh-Hebbweightlearningrule.Hebb權值學習規則learnhd-Hebbwithdecayweightlearningrule.有衰減Hebb權值學習規則learnis-Instarweightlearningfunction.Instar權值學習函數learnk-Kohonenweightlearningfunction.Kohonen權值學習函數learnlv1-LVQ1weightlearningfunction.LVQ1權值學習函數learnlv2-LVQ2.1weightlearningfunction.LVQ2.1權值學習函數learnos-Outstarweightlearningfunction.Outstar權值學習函數learnp-Perceptronweight/biaslearningfunction.感知器的權值/偏差學習函數learnpn-Normalizedperceptronweight/biaslearningfunction.標準感知器的學習函數learnsom-Self-organizingmapweightlearningfunction.自組織映射權值學習函數learnsomb-LEARNSOMBatchself-organizingmapweightlearningfunction.LEARNSOM批處理自組織映射權值學習函數learnwh-Widrow-Hoffweight/biaslearningfunction.Widrow-Hoff權值/偏差學習函數nnnetinput-Netinputfunctions.網絡輸入函數netprod-Productnetinputfunction.Product網絡輸入函數netsum-Sumnetinputfunction.Sum網絡輸入函數nnperformance-Performancefunctions.性能函數mae-Meanabsoluteerrorperformancefunction.平均絕對誤差性能函數mse-Meansquarederrorperformancefunction.均方誤差性能函數sae-Sumabsoluteerrorperformancefunction.Sum絕對誤差性能函數sse-Sumsquarederrorperformancefunction.Sum均方誤差性能函數nnprocess-Processingfunctions.處理函數GeneralDataPreprocessing一般數據預處理fixunknowns-Processesmatrixrowswithunknownvalues.用未知值處理矩陣行mapminmax-Mapmatrixrowminimumandmaximumvaluesto[-11].映射矩陣行最小和最大值成[-1,1]mapstd-Mapmatrixrowmeansanddeviationstostandardvalues.映射矩陣行均值和偏差成標準值processpca-Processesrowsofmatrixwithprincipalcomponentanalysis.用主成分分析處理矩陣行removeconstantrows-Removematrixrowswithconstantvalues.用常量移去矩陣行removerows-Removematrixrowswithspecifiedindices.用指定指標移去矩陣行DataPreprocessingforSpecificAlgorithms特定算法數據預處理lvqoutputs-DefinesettingsforLVQoutputs,withoutchangingvalues.為LVQ定義設置，沒有值的改變nnsearch-Linesearchfunctions.線性搜索函數srchbac-One-dimensionalminimizationusingbacktracking.用回溯法一維最小化srchbre-One-dimensionalintervallocationusingBrent'smethod.用Brent法一維區間位置srchcha-One-dimensionalminimizationusingthemethodofCharalambous.用Charalambous方法一維最小化srchgol-One-dimensionalminimizationusinggoldensectionsearch.用黃金分割收索一維最小化srchhyb-One-dimensionalminimizationusingahybridbisection-cubicsearch.用平分三次收索一維最小化nntopology-Topologyfunctions.拓撲函數gridtop-Gridlayertopologyfunction.網格層拓撲函數hextop-Hexagonallayertopologyfunction.六角形層拓撲函數randtop-Randomlayertopologyfunction.隨機層拓撲函數tritop-Trianglelayertopologyfunction.三角形層拓撲函數nntransfer-Transferfunctions.傳遞函數compet-Competitivetransferfunction.競爭傳遞函數elliotsig-Elliotsigmoidtransferfunction.ElliotS型傳遞函數hardlim-Positivehardlimittransferfunction.正的硬界限傳遞函數hardlims-Symmetrichardlimittransferfunction.對稱硬界