Feedforward neural network methodology
Author(s)
Bibliographic Information
Feedforward neural network methodology
（Statistics for engineering and information science）
Springer, c1999
- : hardcover
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Note
Includes bibliographical references (p. [309]-327) and index
Description and Table of Contents
Description
This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
Table of Contents
Objectives, Motivation, Background, and Organization.- Perceptions-Networks with a Single Node.- Feedforward Networks I: Generalities and LTU Nodes.- Feedforward Networks II: Real-Valued Nodes.- Algorithms for Designing Feedforward Networks.- Architecture Selection and Penalty Terms.- Generalization and Learning.
by "Nielsen BookData"
