人工神经网络试题及答案

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1、QuestionOne:TheweightupdatingrulesoftheperceptronandKohonenneuralnetworkarew(n+1)=w(n)+r

2、d(n)x(n);x(n)=inputvector=[+1Jx1(n),x2(n),...,xm(n)Fw(n)=weightvector=[b(n),w^n),w2(n),wm(n)卩b(n)二biasy(n)二actualresponsed(n)=desiredresponser]=learningrateparameter+1)=Wy(w)+7(w)方心)

3、a(6)(X(n)-W丿⑷)QuestionTwo:Thelimitationoftheperceptronisthatitcanonlymodellinearlyseparableclasses.ThedecisionboundaryofRBFislinearwhereasthedecisionboundaryofFFNNisnon-linear•QuestionThree:TheactivationfunctionoftheneuronofthePerceptron,BPnetworkandRBFnetworkarerespecti

4、vely;;.0(v)=+lifv>0-1ifv<0witha>0—av・l+€丿Gaussian尸20("=exp(—丁万)NN3forsomeQ>0QuestionFour:Pleasepresenttheidea,objectivefunctionoftheBPneuralnetworks(FFNN)andthelearningruleoftheneuronattheoutputlayerofFFNN.Youareencouragedtowritedowntheprocesstoproducethelearningrule.wit

5、hq>0Weightupdateofoutputneuron3•・=jbecauseIfjisanoutputneuronthenusingthechainruleweobtain:dEdEde■dy■7=--_e八一I)©(vJon.ejyjej=dj-yjandForjoutputneuron巧予0(Vj)SubstitutinginAwji=wegetAw〃=〃(dj・yj)©，(v丿)儿QuestionFive:PleasedescribethesimilarityanddifferencebetweenHopfieldNNan

6、dBoltzmannmachine.相同：Bothofthemaresingle-layerinter-connectionNNs.Theybothhavesymmetricweightmatrixwhosediagonalelementsarezeroes.不同：ThenumberoftheneuronsofHopfieldNNisthesameasthenumberofthedimension(K)ofthevectordata.Ontheotherhand,BoltzmannmachinewillhaveK+Lneurons.Th

7、erearcLhiddenneuronsBoltzmannmachinehasKneuronsthatservesasbothinputneuronsandoutputneurons(Auto-associationBoltzmannmachine)・QuestionSix:Pleaseexplainthetermsintheaboveequationindetail.PleasedescribetheweightupdatingequationsofeachnodeinthefollowingFFNNusingtheBPlearnin

8、galgorithm.(PPT原题y=(p(net)=(p(wo+wiXi+w2X2))aw2)15(^(w0+WjXj+w2x2)-dy20W]=丄29(%+忖+柿2)-严(％+讣+讣)-”)>2*zz、八—、0(叫+WE+%兀2一”)二0(/7/)—)」=((p(net)_d)0■Aw0=〃(d-(p{nety)0{net)Aw】=r/(d-(p{net))(pet)xtAw2=〃(d-(p{net))(pet)x2AwnW0=w0+Uwz+ZW2=w2+.LQuestionSeven:Pleasetryyourbestt

9、opresentthecharacteristicsofRBFNN.(1)RBFnetworkshaveonesinglehiddenlayer・(2)InRBFtheneuronmodelofthehid