Sensitivity Analysis for Neural Networks

• Κύριοι συγγραφείς: Yeung, Daniel S. (Συγγραφέας), Cloete, Ian (Συγγραφέας), Shi, Daming (Συγγραφέας), Ng, Wing W. Y. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Natural Computing Series,
• Θέματα: Computer science.; Artificial intelligence.; Computer simulation.; Pattern recognition.; Statistical physics.; Dynamical systems.; Engineering design.; Control engineering.; Robotics.; Mechatronics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Control, Robotics, Mechatronics.; Statistical Physics, Dynamical Systems and Complexity.; Pattern Recognition.; Simulation and Modeling.; Engineering Design.

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...

Πίνακας περιεχομένων:

• 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.