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 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.