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检索条件"主题词=Backpropagation Algorithms"

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An update function that speeds up backpropagation learning

An update function that speeds up backpropagation learning

引用

International Joint Conference on Neural Networks (IJCNN)

作者： W. El Deredy N.M. Branston Department of Neurological Surgery Institute of Neurology London UK

We consider a modification of the backpropagation (BP) learning algorithm in which a linear function, directly proportional to the deviation between target values and actual values at the output, is propagated backwards instead of the original nonlinear function. The new algorithm is tested on the odd/even parity function for orders between 4 and 7 and on high- (180) dimensional data derived from NMR spectroscopy of animal tumours. Results suggest that using the linear function, the network converges faster and is more likely to escape from local minima than when the original BP is used.< >

关键词： backpropagation algorithms Nervous system Testing Nuclear magnetic resonance Spectroscopy Animals Tumors Supervised learning Artificial neural networks Pattern recognition

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Reducing the number of multiplies in backpropagation

Reducing the number of multiplies in backpropagation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： K. Boonyanit A.M. Peterson LSI Logic Corporation Milpitas CA USA STAR Lab Department of Electrical Engineering University of Stanford Stanford CA USA

There have been many algorithms to speed up the learning time of backpropagation. However, most of them do not take into consideration the amount of hardware required to implement the algorithm. Without suitable hardware implementation, the real promise of neural network applications will be difficult to achieve. Since multiply dominates computation and is expensive in hardware, this paper proposes a method to reduce the number of multiplies in the backward path of backpropagation algorithm by setting some neuron errors to zero. It proves the convergence theorem by the general Robbins-Monro process, a stochastic approximation process.< >

关键词： Neurons backpropagation algorithms Convergence Large scale integration Logic Neural network hardware Neural networks Stochastic processes Data flow computing Noise reduction

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Hardware implementation of the backpropagation without multiplication

Hardware implementation of the backpropagation without multi...

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Proceedings of the International Conference on Microelectronics for Neural Networks and Fuzzy Systems

作者： J. Cloutier P.Y. Simard Département d'Iformatique et de Recherche Opérationnelle Université Montreal Montreal Canada AT and T Bell Laboratories Inc. Holmdel NJ USA

The backpropagation algorithm has been modified to work without any multiplications and to tolerate computations with a low resolution, which makes it more attractive for a hardware implementation. Numbers are represented in floating-point format with 1 bit mantissa and 2 bits in the exponent for the states, and 1 bit mantissa and 4 bit exponent for the gradients, while the weights are 16 bit fixed-point numbers. In this way, all the computations can be executed with shift and add operations. Large networks with over 100000 weights were trained and demonstrated the same performance as networks computed with full precision. An estimate of a circuit implementation shows that a large network can be placed on a single chip, reaching more than 1 billion weight updates per second. A speedup is also obtained on any machine where a multiplication is slower than a shift operation.

关键词： Hardware backpropagation algorithms Circuits Computer networks Degradation Stochastic processes Computer architecture Equations Robustness

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The application of backpropagation neural networks to predict the results of combat

The application of backpropagation neural networks to predic...

引用

IEEE International Conference on Engineering Management (EMC)

作者： D.A. Jacobs W.O. Hedgepeth Old Dominion University USA U.S. Army Training and Doctrine Command USA

The information age changes how US Army decision makers predict future trends. The Army is responding with innovative technology to help it's planners to understand the complexities of its decisions, and their impacts, as the Army embraces a post Cold War world view. Using neural network technologies is one way to attack this social technical challenge. Several neural networks were developed to predict the outcome of combat battles. The networks were trained with combat battles from a historical database.< >

关键词： Neural networks Military computing Humidity Temperature Databases Humans Jacobian matrices backpropagation algorithms Modems Missiles

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RRANN: a hardware implementation of the backpropagation algorithm using reconfigurable FPGAs

RRANN: a hardware implementation of the backpropagation algo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J.G. Eldredge B.L. Hutchings Department of Electrical and Computer Engineering Brigham Young University Provo UT USA

Field programmable gate arrays (FPGAs) are an excellent technology for implementing neural networking hardware. This paper presents the run-time reconfiguration artificial neural network (RRANN). RRANN is a hardware implementation of the backpropagation algorithm that is extremely scalable and makes efficient use of FPGA resources. One key feature is RRANN's ability to exploit parallelism in all stages of the backpropagation algorithm including the stage where errors are propagated backward through the network. This architecture has been designed and implemented on Xilinx XC3090 FPGAs, and its performance has been measured.< >

关键词： Hardware backpropagation algorithms Field programmable gate arrays Neurons Neural networks Application software Artificial neural networks Runtime Feeds Speech

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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 ...

引用

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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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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Optimal distribution of patterns in a heterogeneous array of transputers for backpropagation networks

Optimal distribution of patterns in a heterogeneous array of...

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International Joint Conference on Neural Networks (IJCNN)

作者： Foo Shou King P. Saratchandran N. Sundararajan School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Training set parallelism and network based parallelism are two popular paradigms for parallelising a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This study analyses how we can optimally distribute the training set on a heterogeneous processor network when the number of patterns in the training set is not an integer multiple of the number of processors. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis, it is found that equal distribution of training patterns among a homogeneous array of transputers is not necessarily the optimal way to allocate the patterns to processors even when the training set is an integer multiple of the number of processors.< >

关键词： Intelligent networks backpropagation algorithms Neural networks Network topology Feedforward neural networks Pattern analysis Pipelines Electronic mail Linear programming Parallel machines

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Choosing among several parallel implementations of the backpropagation algorithm

Choosing among several parallel implementations of the backp...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Petrowski Départment Informatique Institut National de Télécommunications Evry France

Three parallel implementations of the backpropagation algorithm are studied. The performance for each of these implementations is estimated for three prototypes of neural architectures chosen in such a way that they are limit cases of a wide range of feedforward architectures. This study, based on performance analysis validated by experiments, follows two other papers already published.< >

关键词： backpropagation algorithms Neurons Parallel processing Computer networks Neural networks Feedforward systems Iterative algorithms Prototypes Performance analysis Pipelines

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A modified backpropagation algorithm

A modified backpropagation algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： B.K. Verma J.J. Mulawka Politechnika Warszawska Warszawa PL Dept. of Fundamental Electron. Warsaw Univ. of Technol. Poland

A long and uncertain training process is one of the most important problems for a multilayer neural network using the backpropagation algorithm. In this paper, a modified backpropagation algorithm for a certain and fast training process is presented. The modification is based on the solving of the weight matrix for the output layer using theory of equations and least squares techniques.< >

关键词： backpropagation algorithms Multi-layer neural network Neurons Neural networks Equations Least squares methods

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