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检索条件"主题词=Backpropagation Algorithms"

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Dynamically deactivating hidden neurons in a multilayer perceptron neural network

Dynamically deactivating hidden neurons in a multilayer perc...

引用

IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： H. Amin K.M. Curtis B.R. Hayes-Gill Department of Electrical & Electronic Engineering University of Nottingham Nottingham UK

In this paper we present an approach that terminates the processing of hidden nodes, within a multilayer perceptron (MLP) neural network, if they become inactive during the learning process. The determination of the activity and non-activity of hidden nodes are based on the mean deviation of changes in the average derivative of the hidden nodes within an interval of several iterations. A decreasing threshold value is used to evaluate the mean deviation and hence to deactivate the hidden nodes accordingly.

关键词： Neurons Intelligent networks Multilayer perceptrons Neural networks Multi-layer neural network backpropagation algorithms Iterative algorithms Nonlinear equations Mean square error methods Monitoring

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SCNN: a universal simulator for cellular neural networks

SCNN: a universal simulator for cellular neural networks

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： R. Kunz R. Tetzlaff D. Wolf Institut fur Angewandte Physik Universitat Frankfurt Germany

In this paper a universal simulator for cellular neural network (CNN) is presented. CNN with nonlinear and delay-type templates can be simulated precisely with SCNN, practically without any limitations. Furthermore different training algorithms for networks with translation variant and invariant templates are implemented in SCNN. As an example, parameter deviations of a template have been reduced by training. Simulation and training results are discussed in detail.

关键词： Cellular neural networks Computational modeling Equations Delay Image processing Hardware backpropagation algorithms Signal processing Testing Personal communication networks

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Neural Networks for the Design and Reverse Engineering of BJTs

Neural Networks for the Design and Reverse Engineering of BJ...

引用

European Conference on Solid-State Device Research (ESSDERC)

作者： Ryan Ferguson David J. Roulston Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

We have developed a neural network based software tool, REED (Rapid Engineering of Electron Devices) that maps an electrical description of a bipolar transistor to its physical structure, as described by mask geometri... 详细信息

关键词： Neural networks Reverse engineering SPICE Doping profiles backpropagation algorithms Semiconductor process modeling Software tools Design engineering Electrons Bipolar transistors

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Suspiciousness of loading problems

Suspiciousness of loading problems

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： P. Frasconi M. Gori S. Fanelli M. Protasi Dip. di Sistemi e Informatica Università di Firenze Florence Italy Dip. di Matematica Università di Eiloma Tor Vergata Roma Italy

We introduce the notion of suspect families of loading problems in an attempt of formalizing situations in which classical learning algorithms based on local optimization are likely to fail (because of local minima or numerical precision problems). We show that any loading problem belonging to a non-suspect family can be solved with optimal complexity by a canonical form of gradient descent with forced dynamics (i.e., for this class of problems no algorithm exhibits a better computational complexity than a slightly modified form of backpropagation). The analysis of this paper suggests intriguing links between the shape of the error surface attached to parametric learning systems (like neural networks) and the computational complexity of the corresponding optimization problem.

关键词： Computational complexity Neural networks Shape Multilayer perceptrons backpropagation algorithms Learning systems Atmosphere Convergence Information processing Machine learning

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Advanced neural network training methods for low false alarm stock trend prediction

Advanced neural network training methods for low false alarm...

引用

International Conference on Neural Networks

作者： E.W. Saad D.V. Prokhorov D.C. Wunsch Applied Computational Intelligence Laboiratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

Two possible neural network architectures for stock market forecasting are the time-delay neural network and the recurrent neural network. In this paper we explore two effective techniques for the training of the above networks: the conjugate gradient algorithm and multi-stream extended Kalman filter. We are particularly interested in limiting false alarms, which correspond to actual investment losses. Encouraging results have been obtained when using the above techniques.

关键词： Neural networks Recurrent neural networks Cost function Stock markets Economic forecasting Investments Multilayer perceptrons backpropagation algorithms Computational intelligence Electronic mail

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Comparison of learning algorithms for feedforward neural nets

Comparison of learning algorithms for feedforward neural net...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： J.A. Nossek P. Nachbar A.J. Schuler Institute for Network Theory and Circuit Design Technical University Munich Germany Technische Universitat Munchen Munchen Bayern DE

Several well known learning algorithms for feedforward two-layer neural nets and an improved version of Madaline I have been investigated and compared with respect to learning effort and classification capacity. These results, based on random training patterns, and their significance for generalization have been verified with real life data for ICR/OCR.

关键词： Neural networks Feedforward neural networks backpropagation algorithms Data mining Training data Capacity planning Circuit synthesis Electronic mail Optical character recognition software Supervised learning

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Learning and structural design of feedforward neural networks by employing genetic algorithms

Learning and structural design of feedforward neural network...

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SICE Annual Conference

作者： A.R. Bohari N. Mizuno Department of Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This work is an attempt to face learning and structural design problems simultaneously for feedforward neural networks by employing genetic algorithms and hybrid algorithm. In the learning process, the disadvantages of backpropagation as a learning algorithm can be avoided by using hybrid algorithm. On the other hand the ability of genetic algorithms to perform global search intelligently make this method as a robust learning algorithm, while in the same time design the structure. The proposed algorithm shows good performances where all of the trials of learning processes converge to the desired condition and most of structural design end with desired efficient structure.

关键词： Algorithm design and analysis Neural networks Feedforward neural networks Genetic algorithms Convergence backpropagation algorithms Adaptive algorithm Robustness Space exploration Mechanical engineering

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ScaleNet-multiscale neural network architecture for time series prediction

ScaleNet-multiscale neural network architecture for time ser...

引用

IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： A.B. Geva A.K. Altman Electrical and Computer Engineering Department Ben-Gurion University of the Negev Beersheba Israel Electrical Engineering Department The Physiological Signal Processing Laboratory Technion-Israel Institute of Technology Israel

The effectiveness ofthe multiscale neural network (NN) architecture for time series prediction of nonlinear dynamic systems has been investigated. The prediction task is simplified by decomposing the time series into separate scales of wavelets, and predicting each scale by a separate multilayer perceptron NN. The different scales of the wavelet transform provides an interpretation of the series structures and information about the history of the series, using fewer coefficients than other methods. In the next stage, the predictions of all the scales are combined, applying another perceptron NN, in order to predict the original time series. Each network is trained by the backpropagation algorithm using the Levenberg-Marquadt method. The weights and biases are initialized by new clustering methods, which improved the prediction results compared to random initialization. Three sets of data were analyzed: the sunspots benchmark, fluctuations in a far-infrared laser and a numerically generated series (set A and D in the Santa Fe competition). Taking the ultimate goal to be the accuracy of the prediction, we find that our suggested architecture outperforms traditional nonlinear statistical approaches.

关键词： Neural networks Multilayer perceptrons Wavelet transforms History backpropagation algorithms Clustering methods Data analysis Fluctuations Iron Accuracy

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SHAKE-A multi-criterion optimization scheme for neural network training

SHAKE-A multi-criterion optimization scheme for neural netwo...

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International Conference on Neural Networks

作者： S.S. Pacheco A.G. Thome Computer Department Military Institute of Engineering Rio de Janeiro Brazil Electrical Engineering Department Military Institute of Engineering Rio de Janeiro Brazil

This paper presents a new approach for the task of feedforward type neural network training process based on a multi-criterion efficiency measurement. Here we propose a novel hybrid neuro-genetic algorithm that tries to optimize a three dimension criterion vector composed by speed, accuracy, and percentage of convergence, which measures the overall stability of the training algorithm to converge to good minimal. The proposed approach takes the speed advantage of the conventional algorithms as well as the accuracy and percentage of convergence advantages of the genetic algorithms. The empirical results obtained up to now shows the strength and potentiality of the method.

关键词： Neural networks Pareto optimization Velocity measurement Accidents Military computing Convergence backpropagation algorithms Cost function Genetic algorithms Job production systems

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Neurosolver solves blocks world problems

Neurosolver solves blocks world problems

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International Conference on Neural Networks

作者： A. Bieszczad B. Pagurek Systems and Computer Engineering Carleton University Ottawa Canada

We report an ongoing work on a biologically inspired device, the Neurosolver, introduced in our (1995) earlier paper. To explore and improve the Neurosolver's capabilities, we attempt to apply it to a task of rearranging three different blocks in a blocks world similar to the worlds known from the classic AI literature. The Neurosolver records the observed trajectories in the state space of the blocks world and uses the learned traces to perform searches and construct plans to control the movements of the blocks. This work is a part of a broader effort to devise neurally-based agents that would cooperate en masse in a process of solving complex problems. We consider it a next step toward a Neuromorphic General Problem Solver.

关键词： State-space methods Neural networks Production systems Orbital robotics Path planning Context modeling History Topology Clustering algorithms backpropagation algorithms

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