粒度神经网络对数字语言的数据融合.doc

Zoozvol2912DataFusionofNumeriealLinguistieBasedonGranularNeuralNetworks2(710071)(041004)AbstractThispaperpresentsaneuralnetworksbasedknowledgediseoveryanddatamining(KDDM)methodologybasedongranulareomputingneuraleomputingfuzzyeomputilinguistieeomputingandpatternreeognition.Agranularneuralnetwork(GNN)15designedtodealwithnumerieallinguistiedatafusionandgranularknowledgedis-eoveryinnumerieallinguistiedatabases.TheGNN15abletolearninternalgranularrelationsbetweennumericallin-guistieinputsandoutputsandpredietnewrelationinadatabase.KeordsGranularneuralnetworksDatafusionDataminiKnowledgediseovery1(KnowledgediseoveryanddataMiningKDDM).ljKDDM2(crispNeuralNetworkCNN)CNNCNNKDDMCNN.3.14161.732()10(LinguistieNaturalNetworkLNN)()KDDMKDDMCNN(FuzzynetworkFNN)KDDM(ComputingwithWordsCW)KD-DMsJKDDMt.:CNNFNN;IFThen;.(GramilarneuralnetworkGNN)GNN()IFThen.:1);2)CGNNFGNN;3)CGNNFGNNFGNN2:;().KDDM;.2.1)(6013301)863.:.Web.1(a);.CNNsFNN:.3.GNNGNNsGNNs31CGNNCNNsCGNNCGNN2XY4X(a1b11dl)Y(a2bZcZdZ)(a)12.21(b)(50).1(abd):_xa_dx:C118X1In;-l-l4Cabd(abxfor(a)x(a+)FFFNNKD333aaabCddd3xx:(X/!3FGNN(x)d(3)4abpp:f+(x)forx(a+b)1for(ab)x(a+b)f(x)for(ab)x(4).O.p(df+(x)dx(x)f+(x)(f+(x)(10(f+(x)(1KDDMBPAsCNNsCGNNa.CGNNCGNN:FGNN.32FGNNFNNKDFGNNFNNKD9.FGNN321:XY(albleldi)(azbZczdZ)41.FNNKD:FNNKDs:FNNKDIFNNKDZFNNKD3FNNKD4FNNKDIZxKxlaK:;FNNKDZ2xK:xlbKZ;FNNKD32XK:XlCK:;FNNKD42xKXldK;:(abcd);a.(HeurisitieKnowledgeBasedLearingAlgorithmHLA)FNNKDHLAy(y1):n()P12N:aroundiii=l234681216aroundiii(i/10i/4/2).ZXYxY1234z1211191642167X733?3Xel1.522.533.54Y11.522.3.5xYcNNFGNN14X4(x(7)E():stePII)P=12N.I)12N(kKth)l)ight=12k12stepZ222lll444999111240OJJ:(+:)*:(+:(+(:IV:(+(:(10)(11)V)wLRightRight(t+l)=wRightwL(t+1)=wL(t)XXX(albleld)Y(aZbZeZdZ)Z=XY(abed)lll(l010250.5)1(l)1(l)111()2()2()lll(l010.250.5)3(30.30.751.5)3(30.30.751.5)111()4(40.41.02.0)4(40.41.02.0)222()l(l0.10.2505)2()222(20.2051.0)2()4(40.41.02.0)222()3(30.30751.5)6()222()4(40.41.02.0)8(80.82.04.0)333(30.30.751.5)1(l)3(30.30.751.5)333(30.30.751.5)2()6()333(30.30.751.5)3(30.30.751.5)9()333(30.30.7515)4(40.41.02.0)12(121.2306.0)444(40.41.02.0)1()4(40.4102.0)444(40.41.02.0)2(20.2051.0)8(80.8204.0)444(40.41.02.0)3(30.30.751.5)12(6.0)444(40.41.02.0)4(40.41.02.0)16(161.64.08.0)(12)37X7(13)Ste(FUZZYIFThenrules)FNNKD.FNNKD.step4.CGNNFGNNKDDM.CGNNFGNNKDDM1111111?222?444111.555?222.1333555?1l4.4.14X4l8424.2CGNNBased21CGNN22BPA24CNNs22X(a1blCldl)Y(aZbZcZdZ)Z(abd)CNNIalaZalCNNIZxslXICNN.03;CNNZb1bZb11CNNZZX81XICNN0.027;CNN3elcZ11CNN32X81XICNN.008;CNN4d1d2dCNN42X81XICNN0.006.4CNNCGNN334495CGNN433325CGNN433XXXXXYYYZ=XYYYXXXYYYZXYYY1.1.41(1.410.130.350.68)2.2.59(2.590.030.501.02)3.3.57(3570.220.74147)1.555111139(1390030.0650.13)111.5551.1.88(1.880.040.100.19)1.5552.66(2.660.060.140.29)111.5552.3.48(3480.010.190.38)1.5553334.12(40.67)1115553.5.14(10.84)1.5555.78(5.780.531.102.31)1.2.4(2.741.58)2.5.21(5.210.07)3.7.32(7.320.150.591.19)2.557(5.570230.641.27)4(3.540.371.182.34)2.5.30(5.300541.693.38)0(7.000)2.3337.97(7.970.761.943.88)5(8.850.131.573.16)25554449.76(32.364.76)333331.4.01(4.010.441.292.58)2.7.96(7.960.141.503.01)3.10.68(1061.4.64)3.555lll3.32(3.320.341.062.13)333.5551.5.15(5.150.750.911.84)3.5556.96(6.960.661.693.14)333.5552.LLL8.62(8.620.962.234.56)3.55510.58(1054)333.5553.12.36(12361294.128.23)355513.87(13871.414358.90)1.5.76(5.760.521.052.25)2.9.73(9.730.912.324.74)444443.13.82(13821.354.108.24)5CNN7X.42.53.5111.553.442335.21117.3.910.68881333.558.610.58881236661387775.79.7113.82221GNN6FGNN7X71.231.22222.73.6111.556.82222.57773.7513336.5222788.610.23334.57.510.4444l22236.98.710.488812155513.76666.110.1888113.9111143FGNNBased21FGNN324HLAFGNN33633.236cGNNFGNNCGNN.FGNN.KDDMCGNNFGNN.cGNNFGNNFayyadUMShapiroGPetal.Advaneedinknowledgediseoveryanddatemining.California:AAAI/MITpress1996l35LUHongjunSetionoRudyLiuHuanEffeetivedatamiusingneuralnetworks.IEEETransaetionsonKnowldgeandDataEngi-neering19968(6):957961LinTY.Granulareomputingonbinaryrelationsl:DataminingandneighborhoodsystemsInRoughsetsinKnowledgeseov-eryA.SkowornandL.PolkowskiEds.Berlin.Germany:SPringerVerlage1998.121140JayashiYNakaiM.AutomatedexaetionoffuzzyIFTHENrulesusingneuralnerworks.T.IEEEJapan1990110C(3):198206SugenoMYasukawaTAfuzzylogiebasedapproaehtoqualita-tivemodeling.IEEETrans.FuzzySyst.19931:731YaoYY.GranulareomputingusingneighborhoodsystemsEngi-neeringsignandManufaeturngLondonU.K.ringerVer-lag1999.539553WestphalCBlaxtonT.DataMiningSolutionsMethedandToolsforSolvingRealWorldProblems.NewYOrk:Wil1998YaoYYZhaN.Potentialapplicationsofranulare