Sensitivity Analysis for Neural Networks [recurso electrónico] / by Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng.

Por: Yeung, Daniel S [author.]Colaborador(es): Cloete, Ian [author.] | Shi, Daming [author.] | Ng, Wing W. Y [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Natural Computing SeriesEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: VIII, 86p. 24 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642025327Tema(s): Computer science | Artificial intelligence | Computer simulation | Optical pattern recognition | Engineering design | Computer Science | Artificial Intelligence (incl. Robotics) | Control, Robotics, Mechatronics | Statistical Physics, Dynamical Systems and Complexity | Pattern Recognition | Simulation and Modeling | Engineering DesignFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q334-342TJ210.2-211.495Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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.
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Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos Q334 -342 (Browse shelf(Abre debajo)) 1 No para préstamo 373450-2001

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.

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.

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