differential recurrent neural network based predictive control
Tài liệu A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive doc
... 2002(a)(b)Fig. 12. Volts/Hz-controlled drive dynamic performance with (a) neural- network- based SVM and (b) equivalent DSP -based SVM.the ANN operates at a higher resolution. The network is solvedevery ... VOL. 38, NO. 3, MAY/JUNE 2002Fig. 7. Feedforward neural- network (1–24–12) -based space-vector PWM controller.Fig. 8. Segmentation of neural network output forU-phasePstates.and signals ... to train the neural network. The performanceof acom-plete volts/Hz-controlled drive system is then evaluated with theANN -based SVM and compared with the equivalent DSP -based drive control system....
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neural network-based face detection
... tested this hy-pothesis by using a separate neural network to ar-bitrate among multiple detection networks. It wasfound that the neural network- based arbitrationpro-duces results comparable ... Kanadetk@cs.cmu.eduhttp://www.cs.cmu.edu/˜tkAbstractWe present a neural network- based face detectionsystem. A retinally connected neural network ex-amines small windows of an image, and decideswhether ... constrained artificial neural networks: Applications to visual scene analysisand control. Submitted, 1995.[Hunke, 1994]H. Martin Hunke. Locating andtracking of human faces with neural networks.Master’s...
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rotation invariant neural network-based
... recognition/detection by probabilis-tic decision -based neural network. IEEE Transactions on Neural Networks, Special Issue onArtificial Neural Networks and Pattern Recognition, 8(1), January 1997.[Moghaddam ... Zhang and John Fulcher. Face recognition using artificial neural network group -based adaptive tolerance (GAT) trees. IEEE Transactions on Neural Networks,7(3):555–567, 1996.13Figure 4: Left: Average ... 4: Networks trained with derotated examples, but applied at all 18 orientations.Upright Test Set Rotated Test SetSystem Detect % # False Detect % # False Network 1 90.6% 9140 97.3% 3252Network...
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experimental analysis of neural network based feature extractors for
... Artificial neural networks for feature extractionand multivariate data projection. IEEE Trans. Neural Networks 6,296 –317, 1995.[2] B. Lerner, Toward a completely automatic neural network based human ... 28,Part B, Special issue on artificial neural networks, 544-552, 1998.[3] B. Lerner, H. Guterman, Mayer Aladjem, A comparative study of neural network based feature extraction paradigms. PatternRecognition. ... Multi-MLPMulti-MLP was the one that had 16 neural networks eachof which had only two classes. The training files weredifferent to each of these networks. Each network was0-7803-7278-6/02/$10.00 ©2002...
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neural network-based handwritten signature
... Back-propagation Neural Networks. Proceedings ofthe Sixth International Conference on Document Analysis andRecognition, pp 992, 2001.[10] L. Ma, T. Tan, Y. Wand and D. Zhang. Personal Identification Based ... OER: overall error rate (FAR + FRR).A. Linear Network DevelopmentLinear networks are NNs with no hidden layers ornodes. Despite limitations, these networks provide a use-ful benchmark against ... signatures much better than linearnetworks. The result is a lower OER and a more pro-nounced convergence than was achieved with the linear network (in terms of training the network, see Figure 24)and...
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neural network-based offline handwritten
... on neural network based classification. Then we summarize the classification problems, occurring when dealing with signatures, and propose solutions for them. In this paper a complete neural ... 0.5 1.1 300x300 0.2 0.5 1.4 0.3 2.0 0.5 1.3 330x330 0.1 0.3 1.9 0.2 1.2 0.2 1.7 Neural Network- based Offline Handwritten Signature Verification System using Hu’s Moment Invariant Analysis ... and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-1, October 2011 73 Neural Network- based Offline Handwritten Signature Verification System using Hu’s Moment Invariant Analysis...
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neural network based trajectory planning
... problem. In [8], neural network inverse control techniques are applied for trajectory tracking of a PD controlled rigid robot manipulator. In this study, a neural network based scheme is ... Hydraulic Manipulator Utilizing Neural Networks,” Mechatronics, Vol. 7, No. 4, s. 355-368,1997. [8] Jung S., Hsia T.C., Neural Network Inverse Control Techniques for PD Controlled Robot Manipulator”, ... tracking control problem. The control scheme is shown in figure 2. The neural network operates online to calculate the incremental change in the command values of joint angles, while in the control...
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Tài liệu Neural Network Applications to Manufacturing Processes: Monitoring and Control pptx
... based monitoring and control schemes. (a) A neural identifier combined withan adaptive controller. (b) A gain-tuning neural network controller. (c) A feedforward neural controller combinedwith ... the neural network controller combined with a simple conventionalcontroller, shown in Figure 12.10(c). In this control structure, the conventional controller is used toprovide the network controller ... uN is the network generated control signal and uf is the feedback control signal. In fact, uf can beany of the conventional controllers. The weights of the neural network controller are...
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Tài liệu Thesis "Neural Network Control" doc
... activation function.2Chapter 2 Neural Network Model Based Predictive Control we will use a smaller value as noted below. Predictive Control The algorithm for the predictive controller is discussed in ... the controller’s op-timisation algorithm.plantmodeluyr^ycontrollerFigure 2.1: The neural network based predictive controller Control HorizonAs noted earlier in section 2.1, the control ... theperformance of the controller.Daniel EggertLyngby,den 24. februar 2003ivviiiChapter 2 Neural Network Model Based Predictive Control The aim of controller design is to construct a controller that...
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FUZZY CONTROL AND NEURAL NETWORK
... trích mẫu 0,1 SV : NGUYỄN TÀI TỈNH____ SHSV: 20092747___LỚP: ĐK&TĐH5-K54 FUZZY CONTROL AND NEURAL NETWORK Thiết kế bộ điều khiển nhiệt độ cho phòng làm việc Cấu trúc mạch vòng điều...
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facial expression classification based on multi artificial neural network
... into responsive class using a Neural Network called Sub Neural Network (SNN) of MANN. Lastly, we use MANN’s global frame (GF) consisting some Component Neural Network (CNN) to compose the classified ... reliability coefficients. Our model links many Neural Networks together, so we call it Multi Artificial Neural Network (MANN). 3 Multi Artificial Neural Network apply for image classification 3.1 ... Artificial Neural Network (MANN), applying for pattern or image classification with parameters (m, L), has m Sub -Neural Network (SNN) and a global frame (GF) consisting L Component Neural Network...
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neural network models for formation and control
... 3rightarrow$$;^{\ovalbox{\tt\smallREJECT}}\ovalbox{\tt\smallREJECT}\S^{r}\ovalbox{\tt\smallREJECT}$37Informationsinternallyrepresentedinthebrainareshowninovals.Possiblealgorithmsareshowninparentheses.Fig.2Threeill-posedproblemsinsensory-motor control. Fig.3Arepetitive neural network modellearnsandminimizesenergyforgenerationoftorquewaveformswhichrealizeminimumtorque-changearmtrajectory.Fig.4Twoschemesforlearninginversedynamicsmodelofacontrolledobject.$a$.directinversemodeling.$b$.feedbackerrorlearningscheme.Fig.5Afeedbackerrorlearning neural network model.Theinversedynamicsmodelisacquiredinthethreelayer neural network. 26 Neural Network ModelsforFormationand Control ofMulti-jointArmTrajectory川人光男MitsuoKawatoATRAuditoryandVisualPerceptionResearchLaboratories,Twin21Bldg.MIDTower,Shiromi$2- ... REJECT}$}motor control problemsareill-posedinthesensethatthesolutionisnotunique.andtheproblemisill-posed.thathumanhandshaveexcessdegreesoffreedom.33inputandoutputsthetorque$T_{i}(t)$.Theerrorsignal$s(t)$isgivenasthedifferencebetweentherealtorqueandtheestimatedtorque:$s(t)=T(t)-T_{j}(t)$.ThisapproachtoacquireaninversedynamicsmodeliscalleddirectinversemodelingbyM.Jordan[6].Thedirectinversemodelingdoesnotseemtobeusedinthecentralnervoussystembecauseofthefollowingreasons.First,aftertheinverse-dynamicsmodelisacquired,largescaleconnectionchangemustbedoneforitsinputfromtheactualtrajectorytothedesiredtrajectory,whilepreservingtheminuteone-to-onecorrespondence,sothatitcanbeusedinfeedforward control. Second,weneedothersupervising neural network which,determineswhentheconnectionchangeshouldbedone.Third,thismethodwhichseparatesthelearningand control modescannotcopewithdynamicschangeofacontrolledobject.Fourth,thislearningschemeisnotgoaldirected.Finally,itcannotcopewiththesecondandthe ... problemsinFig.2.M.Jordanexplainedthisreasoninthemanytooneinversekinematicsproblemassociatedwithmotor control ofredundantmanipulatorswithexcessdegreesoffreedom$[6,7]$.Fig.$4b$showsthealternativecomputationalapproachwhichweproposedandcalledasfeedbackerrorlearning.Thisblockdiagramincludesthemotorcortex(feedbackgain$K$andsummationoffeedbackandfeedforwardcommands),thetranscorticalloop(neg-ativefeedbackloop)andthecerebrocerebellum-parvocellularrednucleussystem(inversedynamicsmodel).Thetotaltorque$T(t)$fedtoanactuatorofthemanipulatorisasumofthefeedbacktorque$T_{f}(t)$andthefeedforwardtorque$T_{1}(t)$,whichiscalculatedbytheinverse-dynamicsmodel.Theinverse-dynamicsmodelreceivesthedesiredtrajectory$\theta_{d}$representedinthebodycoordinatessuchasjointanglesormusclelengths,andmonitorsthefeedbacktorque$T_{f}(t)$astheerrorsignal.Thefeedbackerrorlearningschemehasseveraladvantagesoverothermotorlearning34schemesincludingdirectinversemodeling.First,theteachingsignalorthedesiredoutputforthe neural network controllerisnotrequired.Instead,thefeedbacktorqueisusedastheerrorsignal.Second,the control andlearningaredonesimultaneously.Third,back-propagationoftheerrorsignalthroughthecontrolledobjectorthroughaforwardmodelofthecontrolledobject[6]isnotnecessary.Fourth,thelearningisgoaldirected.Finally,itcanresolvetheill-posednessinthesecondandthethirdproblemsinFig.2becauseofgoodcharacteristicsinherentinthefeedbackcontroller.Itisexpectedthatthefeedbacksignaltendstozeroasleamingproceeds.Wecallthislearningschemeasfeedbackerrorlearn$ing$emphasizingtheimportanceofusingthefeedbacktorque(motorcommand)astheerrorsignaloftheheterosynapticlearning.Therearetwopossibilitiesabouthowthecentralnervoussystemcomputesnonlineartransformationsrequiredformakinganinversedynamicsmodelofanonlinearcontrolledobject.Oneisthattheyarecomputedbynonlinear...
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neural network predictive
... Non-Linear Predictive Control Based on a Recurrent Neural Network. In:ERUDIT Conference.Haley, P., D. Soloway and B. Gold (1999). Real-time Adaptive Control Using Neural General-ized Predictive Control. ... Hansen (1999). Neural Networks forModelling and Control of Dynamic Systems.Springer.Tan, Y. and A. Van Cauwenberghe (1996). Non-linear One-step-ahead Control Using Neural Networks: Control Strategy ... possibility of applying neural network predictive control to mobile robottrajectory tracking. The approach presented canbe considered as the inner control loop of a cas-cading control scheme.The...
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báo cáo hóa học: " A biologically inspired neural network controller for ballistic arm movements" ppt
... environment.ConclusionA neural- network motor controller able to simulate theballistic movements of an arm has been presented. Thiscontroller is implemented by means of a neural network that simulates ... of the neural controller. This could possibly bring the neural network to converge to a local minimum state, where theweights are not optimally calibrated to face the problemof the arm control. ... architecture composed by 4 layers. Thedesign process of the neural network used for this study is based on the analysis of the behaviour of various neural structures in responding to a same training and...
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- recurrent neural network library
- recurrent neural network based language model
- recurrent neural network control
- extensions of recurrent neural network based language model
- recurrent neural network based language model bibtex
- recurrent neural network based language modeling in meeting recognition
- recurrent neural network based language model interspeech 2010
- robust recurrent neural network control of biped robot
- adaptive recurrent neural network control of biological wastewater treatment
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