Sensitivity Analysis for Neural Networks

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 t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Yeung, Daniel S. (Autor), Cloete, Ian (Autor), Shi, Daming (Autor), Ng, Wing W. Y. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Natural Computing Series,
Temas:
Artificial intelligence.
Control engineering.
Robotics.
Automation.
System theory.
Pattern recognition systems.
Computer simulation.
Engineering design.
Artificial Intelligence.
Control, Robotics, Automation.
Complex Systems.
Automated Pattern Recognition.
Computer Modelling.
Engineering Design.
Acceso en línea:Texto Completo
Tabla de 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.

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