Sensitivity Analysis for Neural Networks
Springer Berlin (Verlag)
978-3-642-02531-0 (ISBN)
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.
to Neural Networks.- Principles of Sensitivity Analysis.- Hyper-Rectangle Model.- Sensitivity Analysis with Parameterized Activation Function.- Localized Generalization Error Model.- Critical Vector Learning for RBF Networks.- Sensitivity Analysis of Prior Knowledge1.- Applications.
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)| Erscheint lt. Verlag | 18.11.2009 |
|---|---|
| Reihe/Serie | Natural Computing Series |
| Zusatzinfo | VIII, 86 p. 24 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 330 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Adaline • Backpropagation algorithm • Computational Intelligence • Connectionism • Feature Selection • Hyperrectangle model • Knowledge-based artificial neural networks (KBANNs) • learning • Localized generalized error model • machine learning • Multilayer perceptron (MLP) • Neural network design • Neural networks • Neuronale Netze • Optimization • perception • Perceptron • perturbations • RBF (Radial basis function) • Sensitivity Analysis • supervised learning • Unsupervised Learning • Vector learni • Vector learning |
| ISBN-10 | 3-642-02531-5 / 3642025315 |
| ISBN-13 | 978-3-642-02531-0 / 9783642025310 |
| Zustand | Neuware |
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