neural network models for formation and control

... J$$\circ,\Omega$$\vee l/7-$$\eta$26 Neural Network Models for Formation and Control ofMulti-jointArmTrajectory川人光男MitsuoKawatoATRAuditory and VisualPerceptionResearchLaboratories,Twin21Bldg.MIDTower,Shiromi$2- ... 3$\vee$$\dot{\cdot}$Inthis network, nonlineartransformationwasmadeonlyofcascadeoflinearweightedsummation and sigmoidnonlinearity.Thatis,wedidnotuseanyaprioriknowledgeaboutthedynamicalstructureofthecontrolledobject.Thelearningwentwell and the network hassomeextentofgeneralizationcapability.Inthelearning,westillusedthefeedbacktorquecommandastheerrorsignal.SummaryInorderto control voluntarymovements,thecentralnervoussystemmustsolvethefol-lowingthreecomputationalproblemsatdifferentlevels:(1)determinationofadesiredtrajectoryinthevisualcoordinates,(2)transformationoftrajectoryfromvisualcoordi-natestobodycoordinates and (3)generationofmotorcommand.Basedonphysiologicalinformation and previous models, computationaltheoriesareproposed for thefirsttwoproblems, and ahierarchical neural network modelisintroducedtodealwithmotorcom-mand.Combinationofthesecond and thethirdapproachwasfoundtobeveryefficient for learningtrajectory control ofanindustrialroboticmanipulator[14].References[1]Allen,G.I. and Tsukahara,N.(1974).Physiol.Rev.54,957-1006.[2]Atkeson,C.G. and Hollerbach,J.M.(1985).JNeurosci.5,2318-2330.[3]Flash,T. and Hogan,N.(1985).J.Neurosci.5,1688-1703.[4]Fujita,M.(1982).Biol.Cybern.45,195-206.[5]Ito,M.(1970).Intern.J.Neurol.7,162-176.[6]Jordan,M.I. and Rosenbaum,D.A.(1988).COINSTechnicalReport$8\delta- ... k$Trajectoriesderivedfromtheminimumtorque-changemodelarequitedifferentfrom$\backslashi$thoseoftheminimumjerkmodelunderthefollowingbehavioralsituations.(i)Bighor-todeterminetheuniquetrajectorywhichminimizes$C_{T}$.Unoetal.[18]overcamethisdifficultybydevelopinganiterativescheme,sotheuniquetrajectory and theassociatedmotorcommand(torque)canbedeterminedsimultaneously.Thatis,thethreeproblems oftrajectory formation, coordinatestransformation and generationofmotorcommandare.solvedsimultaneouslybythisalgorithm.Mathematically,theiterativelearningschemecanberegardedasaNewton-like...

... processedthrough a logic block to generate the PWM outputs.- for for for for for for for for for for(5)- for for for for for for for for for for(6)664 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, ... Table II and Fig. 4 for all the regions in thesix sectors. For example, the phase-turn-on time expressionsin mode 1 can be derived as- for for for for for for(3)- for for for for for for(4)where and ... 7. Feedforward neural- network (1–24–12)-based space-vector PWM controller.Fig. 8. Segmentation of neural network output for U-phasePstates. and signals only. Similar operations areperformed...

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... reader to (Ford and Fulkerson, 1962; Klein, 1967). 2.2 Alignment models Flows and networks define a general framework in which it is possible to model alignments be- tween words, and to find, ... can simplify the above formula and write: le+l 1 P(a,e,,f,)= ]-I p(ei,f~ilal )i-1 (5) i=1 where a~ -1 is a shorthand for (al, ,ai-1). p(ei, f~,) is a shorthand for p(a~(ei) = f~,) that ... bandwidth to largeur de bande., and one from bandwidth to either largeur or hap.de (type 2), or even through the two edges from bandwidth to largeur and bande (type 3), 2. secondly,...

... initialize and train the network. It maintains a list ofNetworkHelper training data elements.NeuralNetwork A generic neural network. This is a concrete implementation of INeuralNetworkNeuralNetworkCollection ... understand.5.4. A Neural Network In BrainNet libraryNow, let us see how the Neural Network is implemented. Any concrete neural network should implement theINeuralNetwork interface. INeuralNetwork ... BackPropNeuronStrategy (for a backward propagation neural network) .A Neural Network is initialized and connections betweens layers are made by a neural network factory. A Factoryshould implement the INetworkFactory...

... 61,417-425 (1989) Biological Cybernetics 9 1989 A Neural Network Model for Limb Trajectory Formation L. Massone and E. Bizzi Department of Brain and Cognitive Sciences, Massachusetts Institute ... formation. In: Grossberg S (ed) Neural networks and natural intelligence. MIT Press, Cam- bridge, pp 553-622 De Rossi D, Casalino G, Morasso P (1988) Skin and muscles: the basic wetware for ... smoothing is a low-level operation handled by motorneurons. 6 Discussion In this paper we presented a model for the formation of limb trajectories, based on a neural network architec- ture. The...

... of roots r8.2 Laplace and Z TransformsLaplace transforms are an important tool for analysis of continuous time dynamic systems, and Z transforms are an important tool for analysis of discrete ... commands 117.2 Customization of plots . 118 Signals and Systems Commands 118.1 Polynomials . . . 118.2 Laplace and Z Transforms 128.3Frequency responses . . 138.4 Fourier transforms and ... diary command and savedin a diary file. To start recording a diary file during a MATLAB session and to save it infilename, type>> diary filenameTo end the recording of information and to...

... Process Control 18.2 Neural Network Control Charts 18.3 A Hybrid Neural Fuzzy Control Chart 18.4 Design, Operations, and Guidelines for Using the Proposed Hybrid Neural Fuzzy Control Chart ... work for various applications, data transformation is necessary to standardize the raw data into the value rangethat both neural network components can work with. Formulas for data transformation ... a 5–12–12–1 neural network for detecting process variance shift. Data from transformation formulas (Equations 18.1 and 18.2) are fed into M-NN and V-NN, respectively. Both neural networks have...

... includedadequate information for achieving the performancedescribed in the results.3.2. Validation statisticsThe validation statistics is presented for each model and the reference models in Table 2 and ... transformation and inputsselected to the model simply from the bits (0=absence and 1=presence).;; the control parameters defined on the Metal-level GAground of the GEATbx standard settings for ... recentyears, the considerable progress has been in thedeveloping of neural network (NN) models for airquality forecasting (Gardner and Dorling, 1999; Koleh-mainen et al., 2001; Kukkonen et al.,...

... identification and adaptive control. The sense of vision should yield sufficient information about the near and far-ther environment to decide when state control is not so important and when more ... positions and angular orientations for the rotational degrees of freedom. Therefore, for visual dynamic scene understanding, images are considered inter-mediate carriers of data containing information ... requirements for safe and goal-oriented behavior. 1.4.1 Differential Models for Perception “Here and Now” Experience has shown that the simultaneous use of differential and integral models on...