Neural Networks for Applied Sciences and Engineering
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A01=Sandhya Samarasinghe
activation
Author_Sandhya Samarasinghe
Bias Weights
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Codebook Vectors
dimensionality reduction
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ES
Feedback
free
Gauss Newton Method
GRNN
hidden
Hidden Neuron Output
Hidden Neurons
Input Lags
Input Pattern
Input Selection
Input Space
Learning Rate
Linear Neuron
Moisture Content
multilayer
Multiple Linear Regression
Network Output
neuron
neurons
nonlinear
nonlinear clustering
output
Output Neuron
parameter
pattern classification
perceptron
Pruned Network
Recurrent Network
scientific data analysis
sensitivity analysis
Time Series Forecasting
time series modeling
uncertainty assessment in neural networks
Validation RMSE
Weight Update
Weight Vector
Winning Neuron
Product details
- ISBN 9780849333750
- Weight: 929g
- Dimensions: 156 x 234mm
- Publication Date: 12 Sep 2006
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
