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neural network based adaptive controller design of robotic manipulators with an observer 88 doc

neural network based adaptive controller design of robotic manipulators with an observer 88 doc

neural network based adaptive controller design of robotic manipulators with an observer 88 doc

... 12, NO. 1, JANUARY 2001 Neural Network- Based Adaptive Controller Design of Robotic Manipulators with an Observer Fuchun Sun, Member, IEEE, Zengqi Sun, Senior Member, IEEE, and Peng-Yung Woo, Member, ... Woo, Member, IEEEAbstract—A neural network (NN) -based adaptive controller with an observer is proposed in this paper for the trajectorytracking of robotic manipulators with unknown dynamics non-linearities. ... NETWORK- BASED ADAPTIVE CONTROLLER DESIGN 59Fig. 2. Adaptive controller with an observer. C. Observer -Based Controller Design Using Multilayer NNs With A1, a multilayer NNcan approximate themodified...
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Tài liệu A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive doc

Tài liệu A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive doc

... with the ANN -based modulator and compared with that of a conventional DSP -based modulator, and showsexcellent performance. The modulator can be easily applied to avector-controlled drive, and ... drive and eval-uated thoroughly for steady-state and dynamic performance with a conventional DSP -based SVM. The performance of the ANN -based modulator was found to be excellent. Themodulator can ... corresponding toas afunction of angle for both and states in all the phasesand in all the modes can be generated by simulation for traininga neural network. Then,-and-values can besolved from the...
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experimental analysis of neural network based feature extractors for

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 ... Hence, an AA has configuration of d:m: d with d units in both the input and output layers andm<d in the hidden layer.The dimensionality of the input and output is obviouslythe same and the network ... Experimental Analysis of Neural Network Based Feature Extractors forCursive Handwriting RecognitionLing Gang, Brijesh Verma and Siddhi KulkarniSchool of Information Technology,...
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báo cáo hóa học:

báo cáo hóa học:" Research Article Performance Analysis of Bit-Width Reduced Floating-Point Arithmetic Units in FPGAs: A Case Study of Neural Network-Based Face Detector" ppt

... artificial neural networks with the combination of addition andmultiplication on FPGAs [3].The main advantage of the FP format is its wide range.Thefeatureofthewiderangeisgoodforneuralnetworksystems ... forimplementing artificial neural networks on FPGAs,” CanadianJournal of Electrical and Computer Engineering,vol.31,no.1,pp. 30–40, 2006.[4]H.K.Brown,D.D.Cross,andA.G.Whittaker, Neural network number ... drawsconclusions.2. A Neural Network- Based Face Detector Usinga Bit-Width Reduced FPU in an FPGA2.1. General Review on MLP. Aneuralnetworkmodelcanbe categorized into two types: single layer perceptron andmultilayer...
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báo cáo hóa học:

báo cáo hóa học:" Research Article Analysis of the Effects of Finite Precision in Neural Network-Based Sound Classifiers for Digital Hearing Aids" pot

... features (Section 2.1)andthe neural network (Section 2.2). Section 3 will define theconsidered problems: the quantization of the weights of the neural network, and use of approximations for ... performance of the algorithms implemented on the hearing aid, and an improper selection of these values can lead to saturations orlack of precision in the operations of the DSP. This is justone of ... spectral centroid of the ith frame canbe associated with the measure of brightness of the sound,and is obtained by evaluating the center of gravity of thespectrum. The centroid can be calculated...
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neural network-based face detection

neural network-based face detection

... performance for four networksworking alone, the effect of overlap eliminationandcollapsing multiple detections, and the results of us-ing ANDing, ORing, voting, and neural network arbitration. Networks ... replacing that subimage with random noise, and tested the neural network. The resulting sum of squared errors made by the network is an indication of how important that por-tion of the image is for ... setisthenpreprocessed(byapplyinglightingcorrectionandhistogramequalization). A few example imagesare shown in Figure 3. The randomization gives thefilter invariance to translations of less than a pixelandscalingsof10%. Largerchangesintranslationand...
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rotation invariant neural network-based

rotation invariant neural network-based

... containsmany images with faces against complex backgrounds and many images without any faces. Thereare a total of 130 images, with 511 faces (of which 469 are within of upright), and 83,099,211windows ... Moghaddam and Pentland, 1995,Pentland et al., 1994, Burel and Carel, 1994, Colmenarez and Huang, 1997, Osuna et al., 1997,Lin et al., 1997, Vaillant et al., 1994, Yang and Huang, 1994, Yow and Cipolla, ... Y. Kung, and L. J. Lin. Face recognition/detection by probabilis-tic decision -based neural network. IEEE Transactions on Neural Networks, Special Issue onArtificial Neural Networks and Pattern...
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neural network for beginners (part 1 of 3) - codeproject

neural network for beginners (part 1 of 3) - codeproject

... example of the perceptron's inability to solve problems with linearly nonseparable vectors is the boolean XOR problem.Multi layer neural networks With muti-layer neural networks we can solve ... singlelayer networks, let's dicuss those further:Single layer neural networksSingle-layer neural networks (perceptron networks) are networks in which the output unit is independent of the others- ... single layer neural network, the tasks that can be achieved are different fromthose that can be achieved by multi-layer neural networks. As this article is mainly geared towards dealing with singlelayer...
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ai _ neural network for beginners (part 2 of 3) - codeproject

ai _ neural network for beginners (part 2 of 3) - codeproject

... brief, to train a multi-layer Neural Network, the following steps are carried out:Start off with random weights (and biases) in the Neural Network Try one or more members of the training set, see ... :NN_Trainer_XOR : Trains a Neural Network to solve the XOR problemTrainerEventArgs : Training event args, for use with a GUINeuralNetwork : A configurable Neural Network NeuralNetworkEventArgs : Training ... multi-layer neural network to solve the XOR logic problem.A Brief Recap (From part 1 of 3)Before we commence with the nitty gritty of this new article which deals with multi layer Neural Networks,...
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ai_ neural network for beginners (part 3 of 3) - codeproject

ai_ neural network for beginners (part 3 of 3) - codeproject

... population of neural networks. The idea being that the GA will jiggle the weights of the neural networks, within the population, inthe hope that the jiggling of the weights will push the neural network ... singledimension array of NeuralNetwork objects. This can be seen from the constructor code within the GA_Trainer_XORobject://ANN'sprivate NeuralNetwork[] networks;public GA_Trainer_XOR(){ networks ... Trains a neural network to solve the XOR problem using a Microbial GA.TrainerEventArgs: Training event args, for use with a GUI.NeuralNetwork: A configurable neural network. NeuralNetworkEventArgs:...
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