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检索条件"主题词=Backpropagation Algorithms"

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Locked, unlocked and semi-locked network weights for four different camera calibration problems

Locked, unlocked and semi-locked network weights for four di...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Ahmed A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)0780370449

backpropagation training algorithms typically view network weights as a single vector of isotropic parameters to be minimized. In contrast, we present a neural network in which each network weight has its own physical meaning and its different role during network training. The network is used to solve four different types of calibration problems found in computer vision applications. A network weight may be unlocked, locked or semi-locked during training according to the available information about the problem. Experiments show the network trained with the available backpropagation-based algorithms can provide superior results to some other widely-used calibration techniques.

关键词： Cameras Calibration Neural networks Computer vision backpropagation algorithms Application software Multi-layer neural network Feedforward neural networks Optical computing Image processing

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Optimization in companion search spaces: the case of cross-entropy and the Levenberg-Marquardt algorithm

Optimization in companion search spaces: the case of cross-e...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： C.L. Fancourt J.C. Principe Computational NeuroEngineering Laboratory Department of Electrical Engineering University of Florida Gainesville FL USA

We present a new learning algorithm for the supervised training of multilayer perceptions for classification that is significantly faster than any previously known method. Like existing methods, the algorithm assumes a multilayer perceptron with a normalized exponential (softmax) output trained under a cross-entropy criterion. However, this output-criteria pairing turns out to have poor properties for existing optimization methods (backpropagation and its second order extensions) because second-order expansion of the network weights about the optimal solution is not a good approximation. The proposed algorithm overcomes this limitation by defining a new search space for which a second-order expansion is valid and such that the optimal solution in the new space coincides with the original criterion. This allows the application of the Levenberg-Marquardt search procedure to the cross-entropy criterion, which was previously thought applicable only to a mean square error criteria.

关键词： Computer aided software engineering Entropy Multilayer perceptrons backpropagation algorithms Neural engineering Laboratories Optimization methods Mean square error methods Statistics Transfer functions

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A feedforward control system of PMSM based on artificial neural network

A feedforward control system of PMSM based on artificial neu...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Wan Wenbin Zhang Xuedian Xu Jiaqun Tang Renyuan Research Institute of Special Electrical Machines Shenyang University of technology Shenyang China

The paper presents a novel artificial neural network (ANN) based PID speed controller for a permanent magnet synchronous motor (PMSM) drive system. The ANN PID scheme is a MIMO controller and operates using the max-torque/current control strategy A general computer is used as hardware platform. Simulation and experiment results verify the success of a drive system with this ANN PID speed controller.

关键词： Control systems Artificial neural networks Three-term control Neural networks backpropagation algorithms Synchronous motors MIMO Computational modeling Computer networks Transfer functions

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A new approach for fuzzy neural network weight initialization

A new approach for fuzzy neural network weight initializatio...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Abed Rouai M. Ben Ahmed RIADI Laboratory ENSI Tunisia

ISBN: (纸本)0780370449

We develop a method for extracting a fuzzy model directly from input-output data. Our approach is based on three fundamental factors: (1) The use of entropy theory for feature selection, (2) the identification of the fuzzy model structure in one single step by the incremental applying of the fuzzy-c-means algorithm directly to the Cartesian input-output data space, (3) the introduction of a new method "semi-Lambda-cut-density" based on the /spl lambda/-cut concept, for setting the initial weights in neurofuzzy networks (NFN). The NFN is trained by a backpropagation algorithm. A comparative study on benchmark examples is conducted and shows that our method solves the trade-off between the use of a small number of rules and the achievement of a fuzzy model best performance index.

关键词： Fuzzy neural networks Clustering algorithms Input variables Partitioning algorithms Fuzzy systems Entropy Fuzzy set theory Neural networks backpropagation algorithms Iterative algorithms

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Design of neuro-fuzzy controller on DSP for real-time control of induction motors

Design of neuro-fuzzy controller on DSP for real-time contro...

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Tae-Chon Ahn Yang-Won Kwon Hyung-Soo Hwang W. Pedrycz School of Electrical and Electronic Engineering Wonkwang University Iksan Gyeongbuk South Korea Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

This paper deals with the DSP implementation of the high performance induction motor drive that presented on the viewpoint of the design and experiment. The speed control system for the induction motor drive is based on the ANFIS (adaptive network-based fuzzy inference system) controller, that is, a sophisticated neuro-fuzzy controller. This ANFIS controller acts as a feed forward controller that provides the plant with the proper control input and accomplish error backpropagation algorithm through the network. In this paper, the DSP (TMS320F240) has been used to perform the high-speed calculation of the space vector PWM and to build the ANFIS control algorithm. It is confirmed that proposed algorithm provides the more improved control performance for the conventional V/F controller and vector controller. The proposed ANFIS algorithm and DSP technique can be applied to the precise speed control of the induction motor drive system or the field of power electronics.

关键词： Digital signal processing Induction motor drives backpropagation algorithms Velocity control Control systems Adaptive systems Programmable control Adaptive control Fuzzy control Fuzzy neural networks

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Neural learning using AdaBoost

Neural learning using AdaBoost

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International Joint Conference on Neural Networks (IJCNN)

作者： Y.L. Murphey Zhihang Chen Hong Guo Department of Electrical and Computer Engineering The University of Michigan-Dearbom MI

ISBN: (纸本)0780370449

This paper describes a committee of neural networks submitted to the IJCNN 2001 generalization ability challenge (GAC) competition and a number of implementation issues with a focus on the generalization problem. The committee of neural networks was generated using the well-known AdaBoost, which is a general method for improving the performance of any learning algorithm that consistently generates classifiers that perform better than random guessing. We also discuss the feature selection and various experiments conducted in the hope of finding a neural network architecture that can generalize correctly in the blind test data used in the GAC competition.

关键词： Neural networks Neurons Training data backpropagation algorithms Machine learning algorithms Boosting Distribution functions Information processing System testing Decision trees

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A high performance neural-networks-based speech recognition system

A high performance neural-networks-based speech recognition ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Song Yang Meng Joo Er Yang Gao School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780370449

A high performance neural-network-based speech recognition system is presented. A new approach towards feature representation for speech recognition, named state transition matrix (STM), is proposed to address temporal varying problem in speech recognition. Using STM, we need only a single-layer perceptron neural network to perform speech recognition. Experimental results show that an overall accuracy of 95% and 87% was achieved for speaker-dependent isolated word recognition and multi-speaker-dependent isolated word recognition, respectively.

关键词： Speech recognition Neural networks Hidden Markov models Humans Erbium Paper technology Speech processing backpropagation algorithms Network topology Vector quantization

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A new feedforward neural network hidden layer neuron pruning algorithm

A new feedforward neural network hidden layer neuron pruning...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： F. Fnaiech N. Fnaiech M. Najim E. S. S. T. T. Laboratoire CEREP Tunis Tunisia Equipe Signal & Image ENSERB Talence France

This paper deals with a new approach to detect the structure (i.e. determination of the number of hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the hidden layer's neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output layer, this pruning procedure is performed on each node in each layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network hidden layer neurons.

关键词： Neural networks Feedforward neural networks Neurons backpropagation algorithms Iterative algorithms Taylor series Monte Carlo methods Multilayer perceptrons Multi-layer neural network Linear systems

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Appearance-based object recognition using support vector machines

Appearance-based object recognition using support vector mac...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： E.M. dos Santos H.M. Gomes Departamento Sistemas e Computação Universidade Federal Paraíba Campina Grande Paraiba Brazil

Support vector machines (SVM) are a class of algorithms derived from the statistical learning theory that are receiving growing interest by the computer vision community as they present some advantages over classical techniques. This work investigates the ability of SVM to perform appearance-based object recognition. Initial experiments indicated that this may be a promising approach to the problem.

关键词： Object recognition Support vector machines Support vector machine classification Kernel Polynomials Statistical learning Mathematical model Valves Computer vision backpropagation algorithms

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Reconstruction of Compton-camera images using artificial neural networks

Reconstruction of Compton-camera images using artificial neu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： T. Karg J. Pauli G. Anton W. Beulertz Physikalisches Institut IV Friedrich-Alexander-Universität Erlangen-Nümberg Erlangen Germany

We report on a new and fast approach for the three-dimensional reconstruction of X-ray source distributions monitored by a Compton-camera consisting of two layers of pixel-detectors. We use multi-layer feedforward artificial neural networks trained by a standard backpropagation algorithm to determine the probability that a given Compton scattered photon originated from a certain point in the reconstruction space. Summing up this probability for all measured photons at each volume element (voxel) of the discretized reconstruction space gives a good estimate of the real X-ray source distribution. We reconstruct and discuss the point spread function obtained with different materials (germanium, silicon) used for the scatter detector and incident photons having an energy of 122 keV and 512 keV.

关键词： Image reconstruction Electromagnetic scattering Particle scattering X-ray scattering X-ray imaging Monitoring Artificial neural networks backpropagation algorithms Volume measurement Germanium

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