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Sensitivity Analysis for Neural Networks / by Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Natural Computing Series | SpringerLink BücherPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: Online-Ressource (VIII, 86p. 24 illus, digital)ISBN:- 9783642025327
- 006.3
- 006.32
- Q334-342 TJ210.2-211.495
- QA76.87
Contents:
Summary: 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 firPPN: PPN: 1649968191Package identifier: Produktsigel: ZDB-2-SCS
Preface; Contents; 1 Introduction to Neural Networks; 2 Principles of Sensitivity Analysis; 3 Hyper-Rectangle Model; 4 Sensitivity Analysis with Parameterized Activation Function; 5 Localized Generalization Error Model; 6 Critical Vector Learning for RBF Networks; 7 Sensitivity Analysis of Prior Knowledge; 8 Applications; Bibliography
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