Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition
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.
By David M. Scaturo - August 7, 2007
I found Dr. Samarasinghe's very easy to understand yet very comprehensive in its coverage of neural networks. The hand calculations really helped me see how the algorithms are applied to real-world problems. This is one of the best books on the subject that I own, and I own a bunch of them. I highly recommend it!
By C. Bennett - January 16, 2010
I've read a lot of data mining, statistical, and/or computer programming books. I've also read a lot of the scientific literature in the subject area. This is well-written, well-organized, and very clear in terms of communication of information. I've read way too many books in this area that forget that the main point is to actually communicate information effectively, and/or get muddled up in details or fail to explain things from start to finish, don't define concepts, use equations with poorly defined symbology, etc. This is definitely not one of those. I've found it to actually be an enjoyable and enlightening read, which you don't often say for technical books. In my opinion, this is a very well done book.
Required by my Grad Shool
By Tango - March 7, 2008
Extremely expensive, but required by my teacher in grad school in class "Neural Networks". Good, comprehensive source of knowledge to understand NN. However, If you want to use NN to solve just practical problems, read Help in Matlab nntool (2007a), which is decently explained, and follow examples.
This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental ...