Input vector determination by false nearest neighbors

Paul Deignan, Peter Meckl, Matthew Franchek

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In order to generate an accurate mapping, such as a Radial Basis Function (RBF) network, it is necessary to select the inputs which will best predict the dynamics seen in the output. This is not straightforward when either the underlying physics are not precisely known, significant nonlinearities are involved, or variable time delays are present in the mapping. The method of False Nearest Neighbors (FNN) may be used for determining the dimensionality of a system and for inferring the significance of terms of the input vector. Unfortunately, this technique suffers from the curse of dimensionality and can be computationally intensive. This study investigates the potential of the FNN algorithm for identifying the most useful input regressors for a known model when input data and computing time are limited and examines several application issues.

Original languageEnglish
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
Place of PublicationFairfield, NJ, United States
Number of pages6
StatePublished - Dec 1 1997
EventProceedings of the 1997 Artificial Neural Networks in Engineering Conference, ANNIE'97 - St.Louis, MO, USA
Duration: Nov 9 1997Nov 12 1997


OtherProceedings of the 1997 Artificial Neural Networks in Engineering Conference, ANNIE'97
CitySt.Louis, MO, USA

ASJC Scopus subject areas

  • Software


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