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.

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.

This book constitutes the refereed proceedings of the 13th International Scientific Conference on Information Technologies and Mathematical Modeling, named after A.F. Terpugov, ITMM 2014, Anzhero-Sudzhensk, Russia, held in Anzhero-Sudzhensk, Russia, in November 2014. The 50 full papers included in this volume were carefully reviewed and selected from 254 submissions. The papers focus on probabilistic methods and models, queueing theory, telecommunication systems, and software engineering.

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.

Geocomputation may be viewed as the application of a computational science paradigm to study a wide range of problems in geographical systems contexts. This volume presents a clear, comprehensive and thoroughly state-of-the-art overview of current research, written by leading figures in the field. It provides important insights into this new and rapidly developing field and attempts to establish the principles, and to develop techniques for solving real world problems in a wide array of application domains with a catalyst to greater understanding of what geocomputation is and what it entails. The broad coverage makes it invaluable reading for resarchers and professionals in geography, environmental and economic sciences as well as for graduate students of spatial science and computer science.

Growth Hormone and the Heart endeavors to bring together knowledge that has been accumulated in the area of GH and the heart, from basic to clinical studies, by research groups working on this topic throughout the world. Lessons from different experimental models and from several human diseases (acromegaly, adult GH deficiency, heart failure) suggest to endocrinologists and cardiologists that GH may not only have a role in the physiology and pathophysiology of heart function, but that GH itself may have a place in the treatment of primary heart diseases (such as dilated cardiomyopathy) or of cardiac complications of hypopituitarism. Growth Hormone and the Heart will be a useful update of the research produced in the field of cardiovascular endocrinology. The Editors also hope that this book will serve as the primary step in the recognition of the wide physiological and clinical significance of GH and heart interactions.