Sensitivity Analysis for Neural Networks

Daniel S. Yeung

ISBN 10: 3642261396 / ISBN 13: 9783642261398

Published by Springer Berlin Heidelberg, 2012

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks. Seller Inventory # 9783642261398

Bibliographic Details

Title: Sensitivity Analysis for Neural Networks

Publisher: Springer Berlin Heidelberg

Publication Date: 2012

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perception neural networks and radial basis function neural networks.

Review:

From the reviews:

“Neural Networks are seen as an information paradigm inspired by the way the human brain processes information. ... The book may be used by researchers in diverse domains, such as neural networks, machine learning, computer engineering, etc., facing problems connected to sensitivity analysis of neural networks.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1189, 2010)