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检索条件"主题词=Backpropagation Algorithms"

共 1893 条记录，以下是311-320 订阅

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A note on backpropagation, projection learning, and feedback in neural systems

A note on backpropagation, projection learning, and feedback...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： K. Weigl I.N.R.I.A. Sophia Antipolis Sophia-Antipolis France

We show instances where parts of algorithms similar to backpropagation respectively projection learning algorithms have been implemented via feedback in neural systems. The corresponding algorithms, with the same or a similar mathematical expression, do not minimize an error in the output space of the network, but rather in the input space of the network, via a comparison between the function to be approximated and the current approximation executed by the network, which is fed back to the input space: We argue that numerous interlayer resp. intracortical feedback connections, e.g. in the visual primary system of mammals, could serve exactly this purpose. We introduce the paradigm with linear operators for illustration purposes, show the extension to nonlinear operators in function space, introduce projection learning, and discuss future work.< >

关键词： Neurofeedback Gabor filters backpropagation algorithms Pixel Neurons Output feedback Vectors Equations Approximation algorithms Helium

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Newton's Method backpropagation for Complex-Valued Holomorphic Multilayer Perceptrons

Newton's Method Backpropagation for Complex-Valued Holomorph...

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International Joint Conference on Neural Networks

作者： Diana Thomson La Corte Yi Ming Zou Department of Mathematical Sciences Univeristy of Wisconsin-Milwaukee

ISBN: (纸本)9781479914821

The study of Newton's method in complex-valued neural networks faces many difficulties. In this paper, we derive Newton's method backpropagation algorithms for complex-valued holomorphic multilayer perceptrons, and investigate the convergence of the one-step Newton steplength algorithm for the minimization of real-valued complex functions via Newton's method. To provide experimental support for the use of holomorphic activation functions, we perform a comparison of using sigmoidal functions versus their Taylor polynomial approximations as activation functions by using the algorithms developed in this paper and the known gradient descent backpropagation algorithm. Our experiments indicate that the Newton's method based algorithms, combined with the use of polynomial activation functions, provide significant improvement in the number of training iterations required over the existing algorithms.

关键词： Neural networks Newton method backpropagation algorithms Convergence Polynomials Training Vectors

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Principal component analysis based backpropagation algorithm for diagnosis of peripheral arterial occlusive diseases

Principal component analysis based backpropagation algorithm...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Sunil Karamchandani U.B. Desai S.N. Merchant G.D. Jindal Indian Institute of Technology Bombay India Bhabha Atomic Research Center Mumbai India

Impedance cardio-vasography (ICVG) serves as a non-invasive screening procedure prior to invasive and expensive angiographic studies. Parameters like Blood Flow Index (BFI) and Differential Pulse Arrival Time (DPAT) at different locations in both lower limbs are computed from impedance measurements on the Impedance Cardiograph. A backpropagation neural network is developed which uses these parameters for the diagnosis of peripheral vascular diseases such as Leriche's syndrome. The target outputs at the various locations are provided to the network with the help of a medical expert. The paper proposes the use of principal component analysis (PCA) based backpropagation network where the variance in the data is captured in the first seven principal components out of a set of fourteen features. Such a backpropagation algorithm with three hidden layers provides the least mean squared error for the network parameters. The results demonstrated that the elimination of correlated information in the training data by way of the PCA method improved the networks estimation performance. The cases of arterial Narrowing were predicted accurately with PCA based technique than with the traditional backpropagation Technique. The diagnostic performance of the neural network to discriminate the diseased cases from normal cases, evaluated using Receiver Operating Characteristic (ROC) analysis show a sensitivity of 95.5% and specificity of 97.36% an improvement over the performance of the conventional backpropagation algorithm. The proposed approach is a potential tool for diagnosis and prediction for non-experts and clinicians.

关键词： Principal component analysis backpropagation algorithms Neural networks Cardiac disease Cardiovascular diseases Cardiology Blood flow Pulse measurements Impedance measurement Cardiography

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The backpropagation unfold recurrent rule

The backpropagation unfold recurrent rule

引用

International Joint Conference on Neural Networks (IJCNN)

作者： L.-W. Chan P.T.T. Chiu J.Y.W. Chow Computer Science Department Chinese University of Hong Kong Hong Kong China

A novel learning algorithm for recurrent backpropagation (BP) networks is introduced. Existing learning algorithms of the recurrent models are mostly based on the static generalized delta-rule of the BP algorithm which does not give satisfactory results for a recurrent network. The authors derived the BP unfold recurrent rule (BURR) by unfolding the structure of the recurrent network. Using this learning rule, the connecting weights are updated by accumulating the effects of the weight change in each time interval. Experimental results are presented to demonstrate the memorizing ability of a recurrent network using this learning algorithm. Sequences of input characters were successfully recognized.< >

关键词： Feedback loop Output feedback backpropagation algorithms Neural networks Computer science Joining processes Character recognition Neurofeedback Signal processing Recurrent neural networks

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backpropagation versus dynamic programming approach for neural networks learning

Backpropagation versus dynamic programming approach for neur...

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International Conference on Neural Information Processing

作者： M. Krawczak Systems Research Institute Polish Academy of Sciences Warsaw Poland

The learning of multi-layer neural networks can be considered as a special case of a multi-stage optimal control problem. In such a case, the layers are treated as stages and the weights as controls. The problem of optimal weight adjustment is converted into an optimal control problem. The multi-stage optimal control problem can be solved by the application of the dynamic programming method. Within the backpropagation framework, weights are tuned layer-by-layer, as well as step-by-step, in order to minimize the learning error. Meanwhile, within the new algorithm, for each layer, starting from the output layer, a return function is first constructed, and then this function must be minimized with respect to the weights. This procedure is done stage-by-stage (i.e. layer-by-layer).

关键词： Dynamic programming Neural networks Multi-layer neural network Optimal control backpropagation algorithms Neurons Error correction Feedforward neural networks Function approximation Weight control

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An Adaptive Penalty-Based Learning Extension for backpropagation and its Variants

An Adaptive Penalty-Based Learning Extension for Backpropaga...

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International Joint Conference on Neural Networks (IJCNN)

作者： B. Jansen K. Nakayama Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan

Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The technique is easy to implement and computationally inexpensive. In this study, the new approach has been applied to the backpropagation learning algorithm as well as the RPROP learning algorithm and simulations have been performed. The superiority of the new proposed method is demonstrated. By applying the extension, the number of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. Furthermore, the change of the penalty values during training has been studied and its observation shows the active role the penalties play within the learning process.

关键词： backpropagation algorithms Artificial neural networks Cost function Computational modeling Convergence Simulated annealing

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Self-adaptive learning rates in backpropagation algorithm improve its function approximation performance

Self-adaptive learning rates in backpropagation algorithm im...

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International Conference on Neural Networks

作者： U. Bhattacharya S.K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

The backpropagation algorithm helps a multilayer perceptron to learn to map a set of inputs to a set of outputs. But often its function approximation performance is not impressive. In this paper the authors demonstrate that self-adaptation of the learning rate of the backpropagation algorithm helps in improving the approximation of a function. The modified backpropagation algorithm with self-adaptive learning rates is based on a combination of two updating rules-one for updating the connection weights and the other for updating the learning rate. The method for learning rate updating implements the gradient descent principle on the error surface. Simulation results with astrophysical data are presented.

关键词： backpropagation algorithms Function approximation Multilayer perceptrons Approximation algorithms Computer vision Pattern recognition Neural networks Joining processes Computer errors Computational modeling

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Bank Indonesia Interest Rate Prediction and Forecast With backpropagation Neural Network

Bank Indonesia Interest Rate Prediction and Forecast With Ba...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Rini Sovia Musli Yanto Rima Liana Gema Rizki Fernando Universitas Putra Indonesia YPTK padang Padang

ISBN: (纸本)9781538656945;9781538656938

The BI Rate is a policy interest rate that plays a role in directing the movement of the national economy. The problem that arises in the study is to determine a forecast for the movement of the BI Rate. Predictions of bank interest rates can be done with various techniques and methods, one of which uses backpropagation artificial neural networks. This method is a branch of artificial intelligence that has the same process carried out by human brain tissue. The method of working method starts from analyzing the data to be used. The process starts from determining the variables namely: Dollar Exchange Rate, Amount of Money Supply, Inflation, and JCI. The process of backpropagation artificial neural network calculation is continued until the final stage of the process is to find the network output which is used as a forecasting number. The author uses Matlab Software that can determine the weight and bias values. The network architecture used is 4 input layers, 2 hidden layers, and 1 output layer and the desired target is the interest rate number.

关键词： Artificial neural networks backpropagation Training Economic indicators Neurons backpropagation algorithms

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Multi-Spectral Image Classification Using Improved backpropagation Neural Networks

Multi-Spectral Image Classification Using Improved Backpropa...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： R. Li Huaxiao Si Department of Electrical Engineering University of Nebraska Lincolnshire NE USA

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backpropagation-Based Background Compensation of Frequency Interleaved ADC for Coherent Optical Receivers

Backpropagation-Based Background Compensation of Frequency I...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Leandro Passetti Agustin C. Galetto Diego J. Hernando Damian Morero Benjamin T. Reyes Mario R. Hueda Fundación Fulgor − Romagosa 518 − Córdoba (5000) − Argentina Laboratorio de Comunicaciones Digitales − Universidad Nacional de Cordoba − CONICET Av. Velez Sarsfield 1611 − Cordoba (X5016GCA) − Argentina

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper introduces an adaptive background compensation technique to mitigate errors of the analog signal path of Frequency-Interleaved ADCs (FI-ADC). The key ingredients of the proposed technique are MIMO equalization and the backpropagation algorithm often used to train neural networks. The technique introduced here is applicable to digital receivers such as those used in coherent optical communications. Although compensation of FI-ADCs has been discussed in the technical literature, adaptive background techniques have not been reported so far. The latter are important to automatically compensate errors caused by process, voltage, and temperature variations in CMOS implementations of the data converters and therefore ensure high manufacturing yield. Simulations show that the impairments of the analog signal path are accurately compensated and their effect essentially eliminated, resulting in a high performance ADC system.

关键词： Optical receivers MIMO communication Equalizers Adaptive optics backpropagation algorithms Demodulation

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