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检索条件"主题词=Backpropagation Algorithms"

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A two-step approach for improving efficiency of feedforward Multilayer Perceptrons network

A two-step approach for improving efficiency of feedforward ...

引用

International Conference on Information and Communication Technologies (ICICT)

作者： Shoukat Ullah Zakia Hussain Higher Education Department NWFP Pakistan NWFP Pakistan

An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced to only those attributes which contribute in the training process. Then after, the whole dataset is partitioned into n clusters which are finally fed into multilayer perceptrons network based on backpropagation algorithm to carry out blockwise and parallel training.

关键词： Multilayer perceptrons Artificial neural networks Data mining backpropagation algorithms Neural networks Clustering algorithms Data analysis Psychology System testing Computer networks

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Reconfigurable Circular Dichroism in Wafer-Scale Twisted Stacks of Aligned Carbon Nanotube and Phase Change Material Heterostructures

Reconfigurable Circular Dichroism in Wafer-Scale Twisted Sta...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Jichao Fan Minhan Lou Ruiyang Chen Nina Hong Weilu Gao Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA J.A. Woollam Co. Lincoln NE USA

We experimentally demonstrated reconfigurable circular dichroism in wafer-scale twisted stacks of aligned carbon nanotubes and phase change material heterostructures. The structural parameters of stacks were optimized... 详细信息

ISBN: (纸本)9798350369311

关键词： Phase change materials backpropagation algorithms Carbon nanotubes Lasers and electrooptics Structural engineering Laser transitions Electro-optical waveguides

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Neural networks in control systems

Neural networks in control systems

引用

Mediterranean Electrotechnical Conference (MELECON)

作者： S. Saksida F. Bremsak Faculty of Electrical and Computer Engineering University of Ljubljana Ljubljana Slovenia

A modified backpropagation algorithm with a linear output function is discussed from the viewpoint of its convenience in control tasks. Two methods are presented for teaching neural networks to act as an inverse plant. In the simulation example a discrete integrator was used as the plant. The best results were obtained by a combination of both methods.< >

关键词： Neural networks Intelligent networks Control systems Neurons Artificial neural networks Pattern recognition Transfer functions backpropagation algorithms Signal generators H infinity control

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Isolated speech recognition using artificial neural networks

Isolated speech recognition using artificial neural networks

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： P.D. Polur Ruobing Zhou Jun Yang F. Adnani R.S. Hobson Department of Biomedical Engineering Virginia Commonwealth University Virginia USA Department of Electrical Engineering Virginia Commonwealth University Virginia USA

In this project artificial neural networks are used as research tool to accomplish automated speech recognition of normal speech. A small size vocabulary containing the words YES and NO is chosen. Spectral features using cepstral analysis are extracted per frame and imported to a feed-forward neural network which uses a backpropagation with momentum training algorithm. The network is trained to recognize and classify the incoming words into the respective categories. The output from the neural network is loaded into a pattern search function, which matches the input sequence with a set of target word patterns. The level of variability in input speech patterns limits the vocabulary and affects the reliability of the network. The results from the first stage of this. work are satisfactory and thus the application of artificial neural networks in conjunction with cepstral analysis in isolated word recognition holds promise.

关键词： Speech recognition Artificial neural networks Vocabulary Cepstral analysis Neural networks Feedforward systems Feedforward neural networks backpropagation algorithms Pattern matching Impedance matching

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Parallel tangent methods with variable stepsize

Parallel tangent methods with variable stepsize

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International Joint Conference on Neural Networks (IJCNN)

作者： Y.G. Petalas M.N. Vrahatis Department of Mathematics University of Patras Artificial Intelligence Research Center (UPAIRC) Patras Greece

The most widely used algorithm for training multilayer feedforward neural networks is backpropagation. Back-propagation is an iterative gradient descent algorithm. Since its appearance, various methods which modify the conventional BP have been created to improve its efficiency. One such algorithm which uses an adaptive learning rate is backpropagation with variable stepsize, is proposed. Parallel tangent methods are used in global optimization to modify and improve the simple gradient descent algorithm by using from time to time the difference between the current point and the point before two steps as a search direction, instead of using the gradient. In this study, we investigate the combination of the BPVS method with the parallel tangent approach for neural network training. We perform experimental results on well-known test problems to evaluate the efficiency of the method.

关键词： backpropagation algorithms Iterative algorithms Neural networks Feedforward neural networks Error correction Equations Mathematics Artificial intelligence Electronic mail Artificial neural networks

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Artificial neural networks implementation on vectorial supercomputers

Artificial neural networks implementation on vectorial super...

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International Joint Conference on Neural Networks (IJCNN)

作者： E. Sanchez S. Barro C.V. Regueiro Departamento de electrónica y computación. Facultad de Fisica Universidade de Santiago de Compostela Santiago de Compostela Spain

We present the results from the implementation of a multilayer perceptron with the backpropagation algorithm on a FUJITSU VP-2400/10 vectorial supercomputer. The programming methodology employed, tries to obtain the maximum performance in this kind of machines based on: input/output structures, treatment of conditional statements, do-loops vectorization and compiler directives. In this work, computing times for the VU (vectorial unit) and CPU are presented. These times indicate the possibility of real time operation in applications which demand artificial neural networks with a high structural complexity.< >

关键词： Artificial neural networks Supercomputers Neurons Multilayer perceptrons Nonhomogeneous media backpropagation algorithms Speech recognition Computer networks Very large scale integration Equations

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A neural network approach for the control of a tracking behavior

A neural network approach for the control of a tracking beha...

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International Conference on Advanced Robotics (ICAR)

作者： K. Berns R. Dillmann Gruppe Interaktive Planungstechnik Forschungszentrum Informatik an der Universität Karlsruhe Karlsruhe Germany

The problem of correctly evaluating noisy and incorrect data for the interpretation of ultrasonic sensor signals is an often encountered one. Neural networks, with their inherent characteristics of adaptivity and high fault- and noise-tolerance are well suited for such tasks. In this paper two neural network approaches are described for the control of the tracking behavior of an autonomous mobile robot. Input data are provided by a set of ultrasonic sensors mounted at the front of the vehicle. Two neural network learning strategies: backpropagation and reinforcement learning, are examined in a simulation and compared with respect to learning speed, capacity of tracking and the effort required to adapt the control networks of the real vehicle.< >

关键词： Neural networks Sensor phenomena and characterization Mobile robots Remotely operated vehicles Learning Ultrasonic variables measurement Sensor systems backpropagation algorithms Testing Telephony

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Optimization of trading systems and portfolios

Optimization of trading systems and portfolios

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Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr)

作者： J. Moody Lizhong Wu CSE Department Oregon Graduate Institute Portland OR USA

We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning algorithms. The objective functions that we consider as evaluation functions for reinforcement learning are profit or wealth, economic utility, the Sharpe ratio, and our proposed Differential Sharpe Ratio. The trading and portfolio management systems require prior decisions as input in order to properly take into account the effects of transactions costs, market impact, and taxes. This temporal dependence on system state requires the use of reinforcement versions of standard recurrent learning algorithms. We present empirical results in controlled experiments that demonstrate the efficacy of some of our methods. We find that maximizing the differential Sharpe ratio yields more consistent results than maximizing profits, and that both methods outperform a trading system based on forecasts that minimize MSE.

关键词： Portfolios Economic forecasting Costs Investments Supervised learning Finance Labeling backpropagation algorithms Management training Optimization methods

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Edge representation and recognition using neural networks

Edge representation and recognition using neural networks

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ohjae Kwon Chulhee Lee Department Electrical and Electronic Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780370902

In this paper, we propose a new approach to represent and recognize edges of objects using multilayer feedforward neural networks. First, we show how then edge of an object can be represented by neural networks. This is accomplished by generating two classes consisting of samples that lie on each side of the edge and then by training a neural network to classify the two classes. If the training is successfully accomplished, the resulting neural network will have a decision boundary that matches the edge we want to represent. Second, we will propose a matching algorithm that identifies an arbitrarily rotated and shifted edge. The matching algorithm uses a gradient descent algorithm. The proposed algorithm can be used in the area of object representation and recognition. In addition, we investigate the relationship between the number of hidden neurons and complexity of edges.

关键词： Neural networks Neurons Multi-layer neural network Feedforward neural networks Fingerprint recognition Face recognition Shape Information security backpropagation algorithms

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Sparseness Points Cloud Data Surface Reconstruction Based on Radial Basis Function Neural Network (RBFNN) and Simulated Annealing Arithmetic

Sparseness Points Cloud Data Surface Reconstruction Based on...

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International Conference on Computational Intelligence and Security Workshops (CISW)

作者： Xue-mei Wu Gui-xian Li Wei-min Zhao School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China Department of Mechanical Engineering Daqing Petroleum University Daqing China

A novel neural network arithmetic was employed in sparseness points cloud data surface interpolation and reconstruction. Radial basis function neural network and simulated annealing arithmetic was combined. The new arithmetic can approach any nonlinear function by arbitrary precision, and also keep the network from getting into local minimum. Global optimization feature of simulated annealing was employed to adjust the network weights. MATLAB program was compiled, experiments on sparseness points cloud data have been done employing this arithmetic, the result shows that this arithmetic can efficiently approach the surface with 10 -4 mm error precision, and also the learning speed is quick and reconstruction surface is smooth. Different methods have been employed to do surface reconstruction in comparison, the sum squared error is 6.7times10 -8 mm employing the algorithmic proposed in the paper, the one is 1.34times10 -6 mm with same parameters employing radial basis function neural network. backpropagation learning algorithm network does not converge until 3500 iterative procedure.

关键词： Clouds Surface reconstruction Radial basis function networks Simulated annealing Arithmetic Iterative algorithms backpropagation algorithms Neural networks Interpolation MATLAB

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