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4 edition of Parallel implementations of backpropagation neural networks on transputers found in the catalog.

Parallel implementations of backpropagation neural networks on transputers

a study of training set parallelism

by P. Saratchandran

  • 78 Want to read
  • 28 Currently reading

Published by World Scientific in Singapore, River Edge, NJ .
Written in English

    Subjects:
  • Parallel processing (Electronic computers),
  • Neural networks (Computer science),
  • Back propagation (Artificial intelligence),
  • Transputers.

  • Edition Notes

    Includes bibliographical references (p. 189-199) and index.

    Statementeditors, P. Saratchandran, N. Sundararajan, Shou King Foo.
    SeriesProgress in neural processing ;, 3
    ContributionsSundararajan, N., Foo, Shou King.
    Classifications
    LC ClassificationsQA76.58 .P3773 1996
    The Physical Object
    Paginationxviii, 202 p. ;
    Number of Pages202
    ID Numbers
    Open LibraryOL975111M
    ISBN 109810226543
    LC Control Number96012119

    R. Rojas: Neural Networks, Springer-Verlag, Berlin, 7 The Backpropagation Algorithm of weights so that the network function ϕapproximates a given function f as closely as possible. However, we are not given the function fexplicitly but only implicitly through some examples. Consider a feed-forward network with ninput and moutput units. DOI: /jcp Corpus ID: Hardware Implementation of Back-Propagation Neural Networks for Real-Time Video Image Learning and Processing @article{MadokoroHardwareIO, title={Hardware Implementation of Back-Propagation Neural Networks for Real-Time Video Image Learning and Processing}, author={Hirokazu Madokoro and .

    K. Kollmann, K. Riemschneider, H. C. Zeider, "On-Chip Backpropagation Training Using Parallel Stochastic Bit Streams", Proceedings of the IEEE International Conference on Microelectronics for Neural Networks and Fuzzy Systems MicroNeuro'96, pp. , Backpropagation and Neural Networks. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 4 - Ap implementations maintain a graph structure, where the nodes implement the forward() / backward() API - Neural networks are not really neural.

      The idea of applying parallel computing techniques in the area of neural networks has been used in the following research papers [18–21]. As shown, the use of distributed computing has been used mainly in the training phase of the neural network, which generally consumes considerable computational resources when neural networks are vast and. purposeneural network learning algorithms, it became astandard and several authorstried to improve its performance in several ways. This has motivated researchers to study parallel implementations as a means to reduce the training time. There is no consensus on how to simulate artificial neural networks on parallel machines.


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Parallel implementations of backpropagation neural networks on transputers by P. Saratchandran Download PDF EPUB FB2

Neural Network Parallel Implementation Neural Network Structure Parallelization Scheme Batch Training These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm by: 8.

ISBN: OCLC Number: Description: xviii, pages ; 23 cm. Contents: 1. Introduction Transputer Topologies for Parallel Implementation Development of a Theoretical Model for Training Set Parallelism in a Homogeneous Array of Transputers Equal Distribution of Patterns Amongst a Homogeneous Array of Transputers Get this from a library.

Parallel implementations of backpropagation neural networks on transputers: a study of training set parallelism. [P Saratchandran; N Sundararajan; Shou King Foo] -- This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers.

The emphasis is on backpropagation learning and training set. System Upgrade on Tue, May 19th, at 2am (ET) During this period, E-commerce and registration of new users may not be available for up to 12 hours.

Progress in Neural Processing Parallel Implementations of Backpropagation Neural Networks on Transputers: A Study of Training Set Parallelism, pp. () No Access Pattern Allocation Schemes Using Genetic Algorithm.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is critical that these models run fast and generate results in real time.

Although a number of implementations of neural networks are available on sequential machines, most of these implementations require an inordinate amount. A performance analysis is presented that focuses on the achievable speedup of a neural network implementation and on the optimal size of a processor network (transputers or multicomputers that.

A study on the implementation of backpropagation neural networks on transputers. Technical Report EEE/CSP/ School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore S.

Foo, P. Saratchandran and N. Sundararajan Parallel implementation of back- propagation on transputers. Proc. The extended Kalman filter (EKF) algorithm has been shown to be advan- tageous for neural network trainings. However, unlike the backpropagation (BP), many matrix operations are needed for the EKF algorithm and therefore greatly increase the computational complexity.

This paper presents a method to do the EKF training on a SIMD parallel machine. Neural networks is an algorithm inspired by the neurons in our brain.

It is designed to recognize patterns in complex data, and often performs the best when recognizing patterns in audio, images or video. Neurons — Connected.

A neural network simply consists of neurons (also called nodes). These nodes are connected in some way. In this paper, we present a parallel simulation of a fully connected multilayered neural network using the backpropagation learning algorithm on a distributed-memory multiprocessor system.

L-Neuro and the architecture of the machines using these chips is the result of many cooperations: M. Duranton and N. Mauduit for the design of the circuit and the boards, F. Aglan, B. Bru, D. Zwierski and H. Frydlender for the software on the chip and for the development of applications, J.

Gobert and J.A. Sirat for fruitful discussions about the architecture, L. Davidovic for his help in the. II: Implementations on a Big General-Purpose Parallel Computer. Implementation of Backpropagation Neural Networks on Large Parallel Computers (Jim Torresen, Shinji Tomita).

III: Special Parallel Architectures and Application Case Studies. Massively Parallel Architectures for Large-Scale Neural Network Computations (Yoshiji Fujimoto).

Artificial Neural Networks (ANN) are a mathematical construct that ties together a large number of simple elements, called neurons, each of which can make simple mathematical decisions. Together, the neurons can tackle complex problems and questions, and provide surprisingly accurate answers.

A shallow neural network has three layers of neurons that process inputs and generate outputs. Back propagation is one of the simplest and most widely used methods for supervised training of multi layer neural networks, which is an extension to LMS (least mean square) algorithm for linear systems.

In this paper we present parallel implementation of multiplayer perceptron (MLP) networks using backpropagation on master-slave architecture. In this paper, we study the impact of multi processor memory systems in particular, the distributed memory (DM) and virtual shared memory (VSM), on the implementation of parallel backpropagation neural network algorithms.

In the first instance, neural network is partitioned into sub neural networks by applying a hybrid partitioning scheme. In the second, each partitioned network is evaluated. training of multi layer neural networks. In this paper we use parallel implementation of Backpropagation (BP) on Master – Slave architecture to recognize speaker independent eleven steady state vowels of British English.

We perform the recognition task on both sequential and parallel implementation. Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation Shuhui Li, and Foo, S.,Parallel Implementations of Backpropagation Neural Networks on Transputers, World Scientific.

Accordingly, there is some interest in the literature in network implementations of this rule, so as to combine its known, good performance with the speed of a massively parallel realization. In this paper, we present a novel neural-network architecture which implements the k-NN rule and whose distinctive feature relative to earlier work is its.

Among types of classification algorithms, Artificial Neural Network (ANN) is proved to be an effective one that can adapt to various research scenarios. In numbers of ANN implementations, backpropagation neural network (BPNN) is the most widely used one due to its excellent function approximation ability.

A typical BPNN usually contains three. complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL.

The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems.A. Rauber, P. Tomisch, D. Merkl, "parSOM: a parallel Implementation of the self organizing map exploiting cache effectsmaking the SOM fit for interactive high-performance data analysis", In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks.tion 5 gives the details of our implementations of the two chosen strategies.

Section 7 evaluates the performance of the strategies, and Section 8 presents our conclusions. 2 Backpropagation Neural Networks A popular type of neural network is the multilayer percep-tron, in which neurons are organized into at least three lay-ers.