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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.
This book covers theoretical aspects as well as recent innovative
applications of Artificial Neural networks (ANNs) in natural,
environmental, biological, social, industrial and automated
systems. It presents recent results of ANNs in modelling small,
large and complex systems under three categories, namely, 1)
Networks, Structure Optimisation, Robustness and Stochasticity 2)
Advances in Modelling Biological and Environmental Systems and 3)
Advances in Modelling Social and Economic Systems. The book aims at
serving undergraduates, postgraduates and researchers in ANN
computational modelling.
This book covers theoretical aspects as well as recent innovative
applications of Artificial Neural networks (ANNs) in natural,
environmental, biological, social, industrial and automated
systems. It presents recent results of ANNs in modelling small,
large and complex systems under three categories, namely, 1)
Networks, Structure Optimisation, Robustness and Stochasticity 2)
Advances in Modelling Biological and Environmental Systems and 3)
Advances in Modelling Social and Economic Systems. The book aims at
serving undergraduates, postgraduates and researchers in ANN
computational modelling.
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Sharks
Hardcover
R98
Discovery Miles 980
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