associative memory by recurrent neural networks with delay elements
báo cáo hóa học: " Synchronization of nonidentical chaotic neural networks with leakage delay and mixed timevarying delays" pptx
... nonidentical chaotic neural networks with time delays. Neural Netw 2009,22:869-874.34. Gan QT, Xu R, Kang XB: Synchronization of chaotic neural networks with mixed time delays. Commun Nonlinear ... of delayed neural networks with infinite gain. IEEE Trans Neural Netw 2005, 16:1449-1463.2. Chen TP, Lu WL, Chen GR: Dynamical behaviors of a large class of general delayed neural networks. Neural ... bidirectional associative memory networks with time delay. Nonlinear Anal 2007, 66:1558-1572.6. Xu SY, Lam J: A new approach to exponential stability analysis of neural networks with time-varying delays....
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evolving recurrent neural networks are super-turing
... of recurrent neural networks is intimately related to the nature of their synapticweights. In particular, neural networks with static rationalweights are known to be Turing equivalent, and recurrent networks ... various kind of neural networks. We will further provethat evolving (rational and real) recurrent neural network arecomputationally equivalent to (non-evolving) real recurrent neural networks. Therefore, ... Turing machines and rational recurrent neural networks ensures that the above recursiveprocedure can indeed be performed by some non-evolvingrational recurrent neural sub-network [5].Since...
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interactive evolving recurrent neural networks
... computa-tional power of interactive recurrent neural networks. Submitted to Neural Comput.Cabessa, J. and Siegelmann, H. T. (2011b). Evolving re-current neural networks are super-Turing. In Interna-tional ... EVOLVING RECURRENT NEURAL NETWORKS ARE SUPER-TURING333 stream osproduced by M . Finally, an ω-translationψ : {0,1}ω−→ { 0,1}≤ωis said to be realizable by some interactive Turing machine with ... particular I-Ev-RNN[R] by definition.5 THE COMPUTATIONALPOWER OF INTERACTIVEEVOLVING RECURRENT NEURAL NETWORKS In this section, we prove that interactive evolving re-current neural networks are computationally...
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the expressive power of analog recurrent neural networks on infinite
... direction by providing a characterization of the computational power of analog recurrent neural networks working on infinite input streams. More precisely, we consider analog recurrent neural networks as ... seminal work by Siegelmann and Sontag about the computational powerof analog recurrent neural networks [8,10,11]. Hence, the consideration of the same model of synchronous analog neural networks ... 2012Communicated by J.N. KokKeywords:Analog neural networks Analog computationTopologyBorel setsAnalytic setsω-AutomataTuring machinesa b s t r a c tWe consider analog recurrent neural networks...
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An analogue recurrent neural networks
... Inthispaperwediscussedtheapplicationof;analogue recurrent neural networktolearnandtracktidynamicsofanindustrialrobot.Theobservationsma(fromthisstudysuggestthatRNNs(similartothoseinFi1)canbeappliedtothecontrolofrealsystemsthmanifestcomplexproperties-specifically,higdimensionality,non-linearityandrequiringcontinuoiaction.Examplesoftheserealsystemsincludeaircricontrol,satellitestabilization,androbotmanipulatcontrol.Weconcludethatrobustcontrollersofpartialobservable(non-Markov)systemsrequirereal-tinelectronicsystemsthatcanbedesignedassingle-chIntegratedCircuits(CMOSIC).Thispaperexploredsutechniquesandidentifiedsuitablecircuits.anhedeg.IatVIII.REFERENCES[1]S.Townley,etal.,"ExistenceandLearningofcenterlineOscillationsin Recurrent Neural Networks& quot;,IEEETrans. Neural Networks 11:luS205-214,2000.ift[21E.Dijk,"Analysisof Recurrent Neural Networks with applicationto:Orspeakerindependentphonemerecognition",M.ScThesis,UniversityorofTwente,June1999.[3]G.Cauwenberghs,"AnAnalogVLSI Recurrent Neural NetworkllyLeamingaContinuous-TimeTrajectory",IEEETrans. Neural ne Networks 7:346-361,Mar.1996.lip[4]M.Morietal.,CooperativeandCompetitiveNetworkSuitableforchCircuitRealization",IEICETrans.Fundamentals,vol.E85-A,No.9,2127-2134,Sept.2002.[5]H.J.Mattausch,etal.,"Compact associative- memory architecture with fullyparallelsearchcapabilityfortheminimumHammingdistance",IEEEJ.Solid-StateCircuits,vol.37,pp.218-227,Feb.2002.[6]G.Indiveri,"AneuromorphicVLSIdeviceforimplementing2-Dselectiveattentionsystems",IEEETrans. Neural Networks, vol.12,pp.1455-1463,Nov.2001.[7]C.K.KwonandK.Lee,"Highlyparallelandenergy-efficientexhaustiveminimumdistancesearchengineusinghybriddigital/analogcircuittechniques",IEEETrans.VLSIsyst.vol.9,pp.726-729,Oct.2001.[8]T.Asai,M.Ohtani,andH.Yonezu,"AnalogIntegratedCircuitsfortheLotka-VolterraCompetitive Neural Networks& quot;,IEEETrans. Neural Networks, vol.10,pp.1222-1231,Sep.1999.[9]Donckers,etal."Designofcomplementarylow-powerCMOSarchitecturesforloser-take-allandwinner-take-all"Procof7"Intconf.onmicroelectronicsfor neural, fuzzyandbio-inspiredsystems,Spain,Apr1999.[10]A.Ruiz,D.H.OwensandS.Townley,"Existence,learningandreplicationoflimitcyclesin recurrent neural networks& quot;,IEEETransactionson Neural Networks, vol.9,pp.651-661,Sept.1998.467 ... FOXadjustabletimeconstantsatthelevelofthesynapticcontributions[5-7].AnalternativetypeofRNNthatcanbedescribed by thedifferentialequationsgivenbelowcanalsobebuilt with theelectronicneuronsdiscussedinthenextsection.Weseethattheaboveschematic(Fig.1)implementsthe neural network with onlytwodynamicneurons(neuroncircuitisshowninFig.2.).Theequationsofthebranchcurrents(ImlandIm2)discussedinthenextsectionsuggestthesynapsesaresuitabletoimplementbothtypesofRNNrepresented by either(1)or(2).Thesimulatednetworkcontainedsixfullyinterconnected recurrent neurons with continuous-timedynamics.Thesimulated neural networkcanbedescribed by ageneralsetofequationssuchastheonesgivenbelow.Nr5',=y'Wi-exp(y,)-ALexp(yj)N=y'+W-(1-A)exp(y,)-2ALexp(yj)(2) with x,(t)theneuronstatevariablesconstitutingtheoutputsofthenetwork,x,(t)theexternalinputstothenetwork,anda(.)asigmnoidalactivationfunction.Thevaluefor-riskeptfixedanduniforminthepresentimplementation.Thereareseveralfreeparamneters,tobeoptimallyadjusted by thelearningprocess.Forexampleifweimplementafullyin-terconnectedRNN,therewillbe36connectionstrengthsWijand-6thresholdsOj.Thesocalledtriggeringnonlinearfunctionoftheneuronsassociated with thisnetworkistakenastanh(x,)andisshownintheFig.1asVI(xi).However,itislikelythatalargerclassoftriggeringfunctions with thesamepropertiesofoddity,boundedness,continuity,monotonicityandsmoothnesscouldbeconsidered.Suchtriggeringfunctionsincludearctan(x),(1I+e-x)1,ex2etc.Inthe463 ... 20053rdIEEEInternationalConferenceonIndustrialInformatics(INDIN)Ananalogue recurrent neural networkfortrajectorylearningandotherindustrialapplicationsGaneshKothapalliEdithCowanUniversity,SchoolofEngineeringandMathematics,Joondalup,WA6027,Australia.e-mail:g.kothapalligecu.edu.auAbstractAreal-timeanalogue recurrent neural network(RNN)canextractandlearntheunknowndynamics(andfeatures)ofatypicalcontrolsystemsuchasarobotmanipulator.Thetaskathandisatrackingprobleminthepresenceofdisturbances. With referencetothetasksassignedtoanindustrialrobot,oneimportantissueistodeterminethemotionofthejointsandtheeffectoroftherobot.Inordertomodelrobotdynamicsweusea neural networkthatcanbeimplementedinhardware.Thesynapticweightsaremodelledasvariablegaincellsthatcanbeimplemented with afewMOStransistors.Thenetworkoutputsignalsportraytheperiodicityandothercharacteristicsoftheinputsignalinunsupervisedmode.Forthespecificpurposeofdemonstratingthetrajectorylearningcapabilities,aperiodicsignal with varyingcharacteristicsisused.Thedevelopedarchitecture,however,allowsformoregenerallearningtaskstypicalinapplicationsofidentificationandcontrol.Theperiodicityoftheinputsignalensuresconvergenceoftheoutputtoalimitcycle.On-lineversionsofthesynapticupdatecanbeformulatedusingsimpleCMOScircuits.Becausethearchitecturedependsonthenetworkgeneratingastablelimitcycle,andconsequentlyaperiodicsolutionwhichisrobustoveranintervalofparameteruncertainties,wecurrentlyplacetherestrictionofaperiodicformatfortheinputsignals.Thesimulatednetworkcontainsinterconnected recurrent neurons with continuous-timedynamics.Thesystememulatesrandom-directiondescentoftheerrorasamultidimensionalextensiontothestochasticapproximation.Toachieveunsupervisedlearningin recurrent dynamicalsystemsweproposeasynapsecircuitwhichhasaverysimplestructureandissuitableforimplementationinVLSI.IndexTerms-Artificial neural network(ANN),ElectronicSynapse,trajectorytracking, Recurrent Neurons.I.INTRODUCTIONRecently,interesthasbeenincreasinginusing neural networks fortheidentificationofdynamicsystems.Feedforward neural networks areusedtolearnstaticinput-outputmaps.Thatis,givenaninputsetthatismappedintoacorrespondingoutputset by someunknownmap,thefeedforwardnetisusedtolearnthismap.Theextensiveuseofthese networks ismainlyduetotheirpowerfulapproximationcapabilities.Similarly, recurrent neural networks arenaturalcandidatesforleamingdynamicallyvaryinginput-output.Forinstance,oneclassof recurrent neural networks whichiswidelyusedaretheso-calledHopfield networks. Inthiscase,theparametersofthenetworkhaveaparticularsymmetricstructureandarechosensothattheoveralldynamicsofthenetworkareasymptoticallystable[1].Iftheparametersdonothaveasymmetricstructuretheanalysisofthenetworkdynamicsbecomesintractable.Despitethecomplexityoftheinternaldynamicsof recurrent networks, ithasbeenshownempiricallythatcertainconfigurationsarecapableoflearningnon-constanttime-varyingmotions.ThecapabilityofRNNsofadaptingthemselvestoleamcertainspecifiedperiodicmotionsisduetotheirhighlynonlineardynamics.Sofar,certaintypesofcyclic recurrent neural configurationshavebeenstudied.Thesetypesof recurrent neural networks arewellknown,especiallyintheneurobiologyarea,wheretheyhavebeenstudiedforabouttwentyyears.Theexistenceofoscillatingbehaviourincertaincellularsystemshasalsobeendocumented[1-3,10].Suchcellularsystemshavethestructureofwhat,inengineeringapplications,hasbecomeknownasa recurrent neural network.Thusthe neural networkbehaviourdependsnotonlyonthecurrentinput(asinfeedforward networks) butalsoonpreviousoperationsofthenetwork[4].II.ANNFORTRAJECTORYTRACKINGInthispaperwetreata neural networkconfigurationrelatedtocontrolsystems.Wedescribeaclassof recurrent neural networks whichareabletolearnandreplicateautonomouslyaparticularclassoftimevaryingperiodicsignals. Neural networks areusedtodevelopamodel-basedcontrolstrategyforrobotpositioncontrol.Inthispaperweinvestigatethefeasibilityofapplyingsingle-chipelectronic(CMOSIC)solutionstotrackrobottrajectories.0-7803-9094-6/05/$20.00@2005IEEE462...
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programming neural networks with encog 2 in java
... Recurrent neural networks are a special class of neural networks where the layers do not simply flow forward, like the feedforward neural networks that are so common. Chapter 12, Recurrent Neural Networks ... xii Programming Neural Networks with Encog 2 in Java Programming Neural Networks with Encog 2 in Java xx Programming Neural Networks with Encog 2 in Java ... will automatically create such a neural network for you. x Programming Neural Networks with Encog 2 in Java vi Programming Neural Networks with Encog 2 in Java Publisher:...
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báo cáo hóa học: " Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks" doc
... the RMSE with respect to the desired kneeEMC vs traditional controllers without fatigueFigure 4EMC vs traditional controllers without fatigue. A comparison of the performance obtained by the ... flexion extension which is composed by a feedforward inverse model and a feedback controller,both implemented using neural networks. The training ofthe networks is conceived to avoid to a therapist ... E, Ferrarin M, FerrignoG: Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movementsare promising for rehabilitation applications. Funct...
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Báo cáo hóa học: "Research Article µ-Stability of Impulsive Neural Networks with Unbounded Time-Varying Delays and Continuously Distributed Delays" doc
... stabilityanalysis for neural networks with time -delay has attracted a large amount of researchinterest, and many sufficient conditions have been proposed to guarantee the stability of neural networks with various ... concerned with the problem of μ-stability of impulsive neural systems with unbounded t ime-varying delays and continuously distributed delays. Some μ-stability criteria arederived by using the ... μ-stability of delayed neural networks with or withoutuncertainties via different approaches. Those results can be applied to neural networks with unbounded time-varying delays. Moreover, few results...
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Báo cáo hóa học: " Research Article Existence and Stability of Antiperiodic Solution for a Class of Generalized Neural Networks with Impulses and Arbitrary Delays on Time Scales" ppt
... generalized neural networks with impulsesand arbitrary delays. This class of generalized neural networks include many continuousor discrete time neural networks such as, Hopfield type neural networks, ... anti-periodic Cohen-Grossberg neural networks with delays and impulses .1. IntroductionIn this paper, we consider the following generalized neural networks with impulses andarbitrary delays on time scales:xΔt ... cellular neural networks, Cohen-Grossberg neural networks, and so on. To the best of our knowledge, theknown results about the existence of anti-periodic solutions for neural networks are all doneby...
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Tài liệu Báo cáo Y học: Prediction of protein–protein interaction sites in heterocomplexes with neural networks ppt
... by its p osition in t heÓ FEBS 2002 Predicting protein–protein interaction sites (Eur. J. Biochem. 269) 1357 Prediction of protein–protein interaction sites in heterocomplexes with neural networks Piero ... the conserved HPD motif is i mplicated inthe interaction with DnaK [41], and one of the residues ofthe motif is also predicted by neural networks. As a whole,the predicted residues indicate the ... feed-forward neural network trained with the standard back-propagation algorithm [35]. The networksystem is trained/tested to predict w hether each surfaceresidue (represented by a C A atom)...
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Báo cáo khoa học: "Association-based Natural Language Processing with Neural Networks" ppt
... con- version process reinforced with a neural net- work handler. The network is used by the neural network handler and word associations are done in parallel with kana-kanji conver- sion. ... context switches. To avoid these problems without increasing computational costs, we propose the use of the associative functionality of neural networks. The use of association is a natural ... constraints in homonym selection. In the same vein, associative information should be considered a weak constraint because associations by neural networks are not always reliable. Pos- sible conflict...
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leg motion classification with artificial neural networks
... systemscontaining neural networks. IEEE Trans. Neural Networ. 1991, 2, 252–262.21. Jordan, M.I.; Jacobs, R.A. Learning to control an unstable system with forward modeling. InAdvances in Neural Information ... decomposition; feature extraction; pattern recognition; artificial neural networks 1. IntroductionSensor networks, particularly wireless sensor networks, have received considerable attention sincethe ... Sensors 2011, 11 174325. Haykin, S. Neural Networks: A Comprehensive Foundation; Prentice Hall: Upper Saddle River,NJ, USA, 1999.26. Bishop, C.M. Neural Networks for Pattern Recognition; Oxford...
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Báo cáo hóa học: " Research Article Class-Based Fair Code Allocation with Delay Guarantees for OVSF-CDMA and VSF-OFCDM in Next-Generation Cellular Networks" ppt
... thewindow rate degradation, denoted by WRDi,j,forwindowj by subtracting the minimum of Ri,avgand Ri,rcvfrom Ri,avgand then by dividing the resultant by Ri,avg(i.e., WRDi,j=(Ri,avg− ... conditions, and (ii) a packet has a delay bound of 1 packet transmission time, which is equalto the size of the packet divided by the requested averagerate of the flow. With these assumptions, Figure ... these assumptions, Figure 6 showsthe probability of delay violations experienced by real-timeflows. In case of CFCA and (CFC-DBA), the number of delay violations of real-time packets is 0 at all...
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Báo cáo hóa học: " Research Article Random Field Estimation with Delay-Constrained and Delay-Tolerant Wireless Sensor Networks" pptx
... pagesdoi:10.1155/2010/102460Research ArticleRandom Field Estimat ion with Delay- Constrained and Delay- Tolerant Wireless Sensor Networks Javier Matamoros and Carles Ant´on-HaroCentre Tecnol`ogic ... estimation with wireless sensor networks. We consider two encoding strategies,namely, Compress-and-Estimate (C&E) and Quantize-and-Estimate (Q&E), which operate with and without side ... interest: delay- constrained networks, in which the observationscollected in a particular timeslot must be immediately encoded and conveyed to the Fusion Center (FC); delay- tolerant (DT) networks, ...
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Tìm thêm: hệ việt nam nhật bản và sức hấp dẫn của tiếng nhật tại việt nam xác định các mục tiêu của chương trình xác định các nguyên tắc biên soạn khảo sát các chuẩn giảng dạy tiếng nhật từ góc độ lí thuyết và thực tiễn khảo sát chương trình đào tạo gắn với các giáo trình cụ thể tiến hành xây dựng chương trình đào tạo dành cho đối tượng không chuyên ngữ tại việt nam điều tra đối với đối tượng giảng viên và đối tượng quản lí điều tra với đối tượng sinh viên học tiếng nhật không chuyên ngữ1 khảo sát thực tế giảng dạy tiếng nhật không chuyên ngữ tại việt nam xác định mức độ đáp ứng về văn hoá và chuyên môn trong ct phát huy những thành tựu công nghệ mới nhất được áp dụng vào công tác dạy và học ngoại ngữ mở máy động cơ rôto dây quấn đặc tuyến hiệu suất h fi p2 đặc tuyến mômen quay m fi p2 đặc tuyến tốc độ rôto n fi p2 đặc tuyến dòng điện stato i1 fi p2 động cơ điện không đồng bộ một pha thông tin liên lạc và các dịch vụ từ bảng 3 1 ta thấy ngoài hai thành phần chủ yếu và chiếm tỷ lệ cao nhất là tinh bột và cacbonhydrat trong hạt gạo tẻ còn chứa đường cellulose hemicellulose chỉ tiêu chất lượng 9 tr 25