Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
3. Hyper-Rectangle Model
verfasst von : Prof. Daniel S. Yeung, Prof. Ian Cloete, Prof. Daming Shi, Dr. Wing W.Y. Ng
Erschienen in: Sensitivity Analysis for Neural Networks
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In this chapter, we discuss a hyper rectangle model, instead of the traditional hypersphere, which is employed as the mathematical model to represent an MLP’s input space. The hyper-rectangle approach does not demand that the input deviation be very small as the derivative approach requires, and the mathematical expectation used in the hyper-rectangle model reflects the network’s output deviation more directly and exactly than the variance does. Moreover, this approach is applicable to the MLP that deals with infinite input patterns, which is an advantage of the MLP over other discrete feedforward networks like Madalines.