Download Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition PDF EPUB
Author: Author
Pages: 570
Size: 2.668,81 Kb
Publication Date: September 12,2006
Category: Systems Analysis & Design
In response to the exponentially increasing have to analyze vast levels of data, Neural Networks for SYSTEMS and Engineering: From Fundamentals to Complicated Pattern Acknowledgement provides scientists with a straightforward but systematic introduction to neural networks.
You start with an introductory conversation on the part of neural systems in scientific data evaluation, this book offers a solid base of simple neural network ideas. Her neural networks analysis targets theoretical understanding and developments and also practical implementations. Afterwards chapters present a thorough coverage on Personal Organizing Maps for non-linear data clustering, recurrent systems for linear nonlinear period series forecasting, and additional network types ideal for scientific data evaluation.
With a straightforward to comprehend format using comprehensive graphical illustrations and multidisciplinary scientific context, this publication fills the gap searching for neural systems for multi-dimensional scientific data, and relates neural systems to stats. Examines in-depth neural systems for linear and non-linear prediction, classification, clustering and forecasting
§ Illustrates all phases of model advancement and interpretation of outcomes, which includes data preprocessing, data dimensionality decrease, input selection, model advancement and validation, model uncertainty evaluation, sensitivity analyses on inputs, mistakes and model parameters
Sandhya Samarasinghe attained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, United states.
Features
§ Explains neural systems in a multi-disciplinary context
§ Uses comprehensive graphical illustrations to describe complex mathematical ideas for fast and simple understanding
? It includes a synopsis of neural network architectures for useful data analysis accompanied by extensive step-by-step insurance coverage on linear networks, along with, multi-layer perceptron for non-linear prediction and classification explaining all phases of digesting and model advancement illustrated through practical good examples and case studies.