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检索条件"主题词=Backpropagation algorithms"

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An FPGA Accelerator for Embedded Microcontrollers Implementing a Ternarized backpropagation Algorithm

An FPGA Accelerator for Embedded Microcontrollers Implementi...

引用

International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Tatsuya Kaneko Hiroshi Momose Tetsuya Asai Faculty of Information Science and Technology Graduate School Hokkaido University Sapporo Japan

ISBN: (纸本)9781728119588

To better utilize artificial intelligence (AI) in the edge domain, such that it is more attractive and fruitful, the development of low-power and -resource AI devices dedicated to online learning is very important issue to be solved. We proposed new ternarized backpropagation (TBP) algorithms [1] and verified to be favorably compatible with fixed-point 16 bit backpropagation (FixedBP). In this paper, we present our implementation method of TBP on an FPGA as an accelerator for embedded microcontrollers, and evaluate the TBP on the FPGA to achieve a 15.7 % reduction of the logic elements of the FPGA, 12.3 % reduction of the registers, 90.9 % reduction of the multipliers, and 49.8 % reduction of the SRAM usage, comparing with those for the FixedBP. We verify its capabilities for training MNIST classification task with a mini-batch size of one. In addition, we demonstrate image recognition system as application example.

关键词： artificial intelligence backpropagation field programmable gate arrays fixed point arithmetic image classification learning (artificial intelligence) microcontrollers Field programmable gate arrays Fixed-point arithmetic back propagation Microcontrollers Artificial Intelligence Image classification backpropagation algorithms tributyl phosphate

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A weight value initialization method for improving learning performance of the backpropagation algorithm in neural networks

A weight value initialization method for improving learning ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： H. Shimodaira Nihon MECCS Co. Ltd. Tokyo Japan

In this paper, we propose a new method (the OIVS method) for initializing weight values, which is based on the equations representing the characteristics of the information transformation mechanism of a node. Numerical simulations show that the learning performance of the OIVS method is superior to that of the conventional method. It should be noted that if we use appropriate values of the parameters in the OIVS method, the nonconvergence case can be avoided.< >

关键词： Equations Neural networks Multi-layer neural network backpropagation algorithms Numerical simulation

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backpropagation algorithm for multiresolution image classification

Backpropagation algorithm for multiresolution image classifi...

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IEEE International Conference on Image Processing

作者： H. Osman S.D. Blostein Department of Electrical and Computer Engineering Queen's University Kingston ONT Canada

This paper proposes a variation of the standard backpropagation (BP) algorithm that is particularly suitable for training neural networks utilized in multiresolution image classification. The aim is to solve the problem of determining which image resolutions to utilize as network inputs. The approach is to have a mix of image resolutions as network inputs and then apply the proposed BP variant. This variant not only solves the image classification problem, but also removes redundant multiresolution network inputs aiming at attaining only those necessary for classification. Removal of redundant inputs is based on that during the backward phase of BP training the network input units whose inputs are extracted from shared image space inhibit one another. Experimental results that demonstrate the viability of the proposed BP variant are presented.

关键词： backpropagation algorithms Image resolution Image classification Neural networks Cost function Pixel

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Fluctuation analysis of a two-layer backpropagation algorithm used for modelling nonlinear memoryless channels

Fluctuation analysis of a two-layer backpropagation algorith...

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Asilomar Conference on Signals, Systems & Computers

作者： N.J. Bershad M. Ibnkahla G. Blauwens J. Cools A. Soubrane N. Ponson Department of Electrical and Computer Engineering University of California Irvine CA USA National Polytechnics Institute of Toulouse ENSEEIHT GAPSE Toulouse France Department of Electrotechnology-ESAT Catholic University of Louvain Belgium

Neural networks have previously been used for modelling the nonlinear characteristics of memoryless nonlinear channels using the backpropagation learning (BP) with experimental training data (Ibnkahla et al. 1997). The mean transient and convergence behavior of a simplified two-layer neural network have also been studied (Bershad et al. 1997). The network was trained with zero mean Gaussian data. This paper extends these results to include the effects of the weight fluctuations upon the mean-square-error (MSE). A new methodology is presented which can be extended to other nonlinear learning problems. The new mathematical model is able to predict the MSE learning behavior as a function of the algorithm step size /spl mu/. Linear recursions are derived for the variance and covariance of the weights which depend nonlinearly upon the mean weights. As in linear gradient search problems (LMS, etc.), there exists an optimum /spl mu/ (minimizing the MSE) which is the trade-off between fast learning and small weight fluctuations. Monte Carlo simulations display excellent agreement with the theoretical predictions for various /spl mu/.

关键词： Fluctuations Algorithm design and analysis backpropagation algorithms Neural networks Training data Convergence Mathematical model Search problems Least squares approximation Displays

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Gain Parameters Based Complex-Valued backpropagation Algorithm for Learning and Recognizing Hand Gestures

Gain Parameters Based Complex-Valued BackPropagation Algorit...

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International Joint Conference on Neural Networks

作者： Yuanshan Liu He Huang Tingwen Huang School of Electronics and Information Engineering Soochow University Texas A&M University at Qatar

ISBN: (纸本)9781479914821

In this paper, an improved complex-valued backpropagation algorithm with gain parameters is proposed. It is then employed to train a complex-valued feedforward neural network with one hidden layer. The well-trained complex-valued neural network is finally applied to deal with the recognition problem of 26 hand gestures. The results of experiment clearly show that much better performance can be achieved by our improved complex-valued backpropagation algorithm than some existing methods.

关键词： Biological neural networks Neurons backpropagation Convergence Joints backpropagation algorithms

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Adaptive equalization using the complex backpropagation algorithm

Adaptive equalization using the complex backpropagation algo...

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International Conference on Neural Networks

作者： Cheolwoo You Daesik Hong Department of Electronic Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780332105

For decreasing intersymbol interference (ISI) due to band-limited channels in digital communication, the uses of equalization techniques are necessary. Among adaptive equalization techniques, because of their ease of implementation and nonlinear capabilities, the neural networks have been used as an alternative for effectively dealing with the channel distortion, especially the nonlinear distortion. The complex backpropagation (BP) neural networks are proposed as nonlinear adaptive equalizers that can deal with both QAM and PSK signals of any constellation size (e.g. 32-QAM, 64-QAM and MPSK), and the complex BP algorithm for the new node activation functions having multi-output values and multi-saturation regions is presented. We also show that the proposed complex BPN provides, compared with the linear equalizer using the least mean squares (LMS) algorithm, an interesting improvement concerning bit error rate (BER) when channel distortions are nonlinear.

关键词： Adaptive equalizers backpropagation algorithms Nonlinear distortion Intersymbol interference Neural networks Bit error rate Digital communication Quadrature amplitude modulation Phase shift keying Constellation diagram

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Neural training of complex sequential associations using recurrent continuous backpropagation

Neural training of complex sequential associations using rec...

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International Conference on Neural Networks

作者： L.H. Chen P.B. Tan M.K. Weir S.K. Foo School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

Proposes a path-based neural network algorithm called recurrent continuous backpropagation two (RCBP-2) for complex sequential processing using a gradient descent method. Under the path-based approach, the goal weights are a collection of weight states. Coupled with the underlying continuity of training exemplars and sequential nature of the system attributes, RCBP-2 can achieve arbitrarily close approximations of complex trajectories within a fixed and relatively small network topology. The performance of RCBP-2 is also monitored by training and subsequently testing on a 4-orbits problem. The results show that RCBP-2 results in a fast and efficient algorithm for complex sequential processing.

关键词： Neural networks Recurrent neural networks backpropagation algorithms Network topology Monitoring Testing

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Evaluation of backpropagation Reconstruction Algorithm for 3D Microwave Imaging of Body Part Anomalies

Evaluation of Backpropagation Reconstruction Algorithm for 3...

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IEEE International Symposium on Antennas and Propagation

作者： Claire Migliaccio Christian Pichot Ian Platt Adrian Tan Ian Woodhead Universite Cote d'Azur CNRS LEAT Sophia Antipolis France Lincoln Agritech Ltd Lincoln Canterbury New Zealand

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166704;9781728166711

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

关键词： Microwave measurement Microwave antennas Three-dimensional displays backpropagation algorithms Reconstruction algorithms Image reconstruction Microwave imaging

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Fast backpropagation learning using steep activation functions and automatic weight reinitialization

Fast backpropagation learning using steep activation functio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： T.-H. Cho R.W. Conners P.A. Araman The Spatial Data Analysis Laboratory Virginia Polytechnic Institute and State University Blacksburg VA USA

Several backpropagation (BP) learning speed-up algorithms that employ the gain parameter, i.e., steepness of the activation function, are examined to determine the effect of increased gain on learning time. It was shown by simulation that although these algorithms can converge faster than the standard BP learning algorithm on some problems, they can be unstable in convergence, i.e., they frequently fail to converge within a finite time. One main reason for this divergence is inappropriate setting of initial weights in the network. To overcome this instability, an automatic random reinitialization of the weights is proposed when convergence speed becomes very slow. BP learning algorithms with this weight reinitialization and larger initial gain (around 2 or 3) were found to be much faster and more stable in convergence than those without weight reinitialization.< >

关键词： Convergence Performance gain Multi-layer neural network Neural networks Data analysis Laboratories backpropagation algorithms Feedforward neural networks Artificial neural networks Neurons

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An Application of backpropagation Neural Network for Sales Forecasting Rice Miling Unit

An Application of Backpropagation Neural Network for Sales F...

引用

International Conference of Computer Science and Information Technology

作者： Mendarissan Aritonang Denny Jean Cross Sihombing Informatics Management Department Methodist University of Indonesia Medan Indonesia Information System Department Atma Jaya Catholic University of Indonesia Jakarta Indonesia

ISBN: (数字)9781728154237

ISBN: (纸本)9781728154244

There are many rice fields and rice milling factories. Rice Milling Units (RMU) are still many who have not applied the prediction method for the sale of rice so that it can affect the availability of raw materials. The purpose of this research is to forecast product sales (rice) at RMU so that it can know the number of raw materials needed so that they avoid idle time. Obtain optimal forecasting; it compared to 2 (two) forecasting methods, Linear Regression, and Artificial Neural network with a backpropagation algorithm. The results showed that the value of MSE on a linear regression method of 0.214, while at the time using Artificial Neural Network obtained an MSE value of 0.00099713. Based on the value of the MSE, the smallest MSE is forecasting by the backpropagation Neural Network method.

关键词： backpropagation Neural Network Linear Regression Sales Forecasting Artificial Inteligence Linear Regression Mean square error Sales back propagation Prediction methods Rice mill grain backpropagation algorithms Artificial neural networks raw material Neural network

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