Sandhya Samarasinghe ... 570 pages - Publisher: Auerbach Publications; (September, 2006) ... Language: English - ISBN-10: 084933375X - ISBN-13: 978-0849333750.
In response to the exponentially increasing need to analyze vast amounts
of data, Neural Networks for Applied Sciences and Engineering: From
Fundamentals to Complex Pattern Recognition provides scientists with a
simple but systematic introduction to neural networks. Beginning
with an introductory discussion on the role of neural networks in
scientific data analysis, this book provides a solid foundation of basic
neural network concepts. It contains an overview of neural network
architectures for practical data analysis followed by extensive
step-by-step coverage on linear networks, as well as, multi-layer
perceptron for nonlinear prediction and classification explaining all
stages of processing and model development illustrated through practical
examples and case studies. Later chapters present an extensive coverage
on Self Organizing Maps for nonlinear data clustering, recurrent
networks for linear nonlinear time series forecasting, and other network
types suitable for scientific data analysis. With an easy to
understand format using extensive graphical illustrations and
multidisciplinary scientific context, this book fills the gap in the
market for neural networks for multi-dimensional scientific data, and
relates neural networks to statistics. Features: Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding § Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting §
Illustrates all stages of model development and interpretation of
results, including data preprocessing, data dimensionality reduction,
input selection, model development and validation, model uncertainty
assessment, sensitivity analyses on inputs, errors and model parameters. Sandhya
Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba
University in Russia and an MS and PhD in Engineering from Virginia
Tech, USA. Her neural networks research focuses on theoretical
understanding and advancements as well as practical implementations.
