Download or read book Parallel Implementations of Backpropagation Neural Networks on Transputers written by P. Saratchandran and published by World Scientific. This book was released on 1996 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
Download or read book Parallel Implementation of Backpropagation Neural Networks written by Arularasan Ramasamy and published by . This book was released on 1997 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Parallel Implementation of a Backpropagation Neural Network and Its Applicability to SPECT Image Reconstruction written by John Patrick Kerr and published by . This book was released on 1992 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Parallel Implementation of a Backpropagation Neural Network and Its Applicability to SPECT Image Reconstruction written by and published by . This book was released on 1992 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this study was to determine the feasibility of using an Artificial Neural Network (ANN), in particular a backpropagation ANN, to improve the speed and quality of the reconstruction of three-dimensional SPECT (single photon emission computed tomography) images. In addition, since the processing elements (PE)s in each layer of an ANN are independent of each other, the speed and efficiency of the neural network architecture could be better optimized by implementing the ANN on a massively parallel computer. The specific goals of this research were: to implement a fully interconnected backpropagation neural network on a serial computer and a SIMD parallel computer, to identify any reduction in the time required to train these networks on the parallel machine versus the serial machine, to determine if these neural networks can learn to recognize SPECT data by training them on a section of an actual SPECT image, and to determine from the knowledge obtained in this research if full SPECT image reconstruction by an ANN implemented on a parallel computer is feasible both in time required to train the network, and in quality of the images reconstructed.
Download or read book Parallel Architectures for Artificial Neural Networks written by N. Sundararajan and published by Wiley-IEEE Computer Society Press. This book was released on 1998-12-14 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: An excellent reference for neural networks research and application, this book covers the parallel implementation aspects of all major artificial neural network models in a single text. Parallel Architectures for Artificial Neural Networks details implementations on various processor architectures built on different hardware platforms, ranging from large, general purpose parallel computers to custom built MIMD machine. Working experts describe their implementation research including results that are then divided into three sections: The theoretical analysis of parallel implementation schemes on MIMD message passing machines The details of parallel implementation of BP neural networks on general purpose, large, parallel computers Four specific purpose parallel neural computer configuration Aimed at graduate students and researchers working in artificial neural networks and parallel computing this work can be used by graduate level educators to illustrate parallel computing methods for ANN simulation. Practitioners will also find the text an ideal reference tool for lucid mathematical analyses.
Download or read book A Parallel Implementation of Backpropagation Neural Network on MASPAR MP 1 written by Faramarz Valafar and published by . This book was released on 1993 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Backpropagation written by Yves Chauvin and published by Psychology Press. This book was released on 2013-02-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The second presents a number of network architectures that may be designed to match the general concepts of Parallel Distributed Processing with backpropagation learning. Finally, the third section shows how these principles can be applied to a number of different fields related to the cognitive sciences, including control, speech recognition, robotics, image processing, and cognitive psychology. The volume is designed to provide both a solid theoretical foundation and a set of examples that show the versatility of the concepts. Useful to experts in the field, it should also be most helpful to students seeking to understand the basic principles of connectionist learning and to engineers wanting to add neural networks in general -- and backpropagation in particular -- to their set of problem-solving methods.
Download or read book Parallel Architectures And Neural Networks Fourth Italian Workshop written by E R Caianiello and published by World Scientific. This book was released on 1991-11-12 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organized and supported by IIASS and other Italian institutions, this Workshop in Vietri sul Mare has now become a permanent annual event. The aim is to foster closer links among all groups and individuals working in Europe in this inter- and multi-disciplinary area. Topics cover models, architectures and applications.
Download or read book Adaptive and Natural Computing Algorithms written by Andrej Dobnikar and published by Springer. This book was released on 2011-04-09 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.
Download or read book Implementation of the Back Propagation Learning Algorithm of Multi layer Neural Networks on a Parallel Machine transputer written by Tak Kiu Chan and published by . This book was released on 1992 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive and Natural Computing Algorithms written by Mikko Kolehmainen and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.
Download or read book Neural Network Parallel Computing written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Download or read book Parallel Computing Technologies written by Viktor Ėmmanuilovich Malyshkin and published by Springer Science & Business Media. This book was released on 2005-08-18 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Parallel Computing Technologies, PaCT 2005, held in Krasnoyarsk, Russia in September 2005. The 38 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on theory, fine-grain parallelism, software, tools, and applications. A broad variety of parallel processing issues and distributed computing in general are addressed as well.
Download or read book An Implementation of Back propagation Learning on GF11 a Large SIMD Parallel Computer written by Michael Witbrock and published by . This book was released on 1989 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Current connectionist simulations require huge computational resources. We describe a neural network simulator for the IBM GF11, and experimental SIMD machine with 566 processors and a peak arithmetic performance of 11 Gigaflops. We present our parallel implementation of the backpropagation learning algorithm, techniques for increasing efficiency, performance measurements on the NETTALK text-to-speech benchmark, and a performance model for the simulator. Our simulator currently runs the back-propagation learning algorithm at 900 million connections per second, where each 'connection per second' includes both a forward and backward pass. This figure was obtained on the machine when only 356 processors were working; with all 566 processors operational, our simulation will run at over one billion connections per second. We conclude that the GF11 is well-suited to neural network simulation, and we analyze our use of the machine to determine which features are the most important for high performance."
Download or read book Parallel Architectures And Neural Networks Third Italian Workshop written by E R Caianiello and published by World Scientific. This book was released on 1990-12-13 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Parallel Implementation on Improved Error Signal of Backpropagation Algorithm written by Teh Noranis Mohd. Aris and published by . This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Networks Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
