Table of Contents
Advances in Artificial Neural Systems
Volume 2016, Article ID 2303181, 11 pages
http://dx.doi.org/10.1155/2016/2303181
Research Article

A State-Based Sensitivity Analysis for Distinguishing the Global Importance of Predictor Variables in Artificial Neural Networks

1Management Department, College of Business, Frostburg State University, Frostburg, MD 21532, USA
2Palm Island Enviro-Informatics LLC, Sarasota, FL 34232, USA
3Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA
4Management Information Systems Department, College of Business, Ohio University, Athens, OH 45701, USA

Received 11 April 2016; Accepted 29 June 2016

Academic Editor: Ozgur Kisi

Copyright © 2016 Ehsan Ardjmand et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Ehsan Ardjmand, David F. Millie, Iman Ghalehkhondabi, William A. Young II, and Gary R. Weckman, “A State-Based Sensitivity Analysis for Distinguishing the Global Importance of Predictor Variables in Artificial Neural Networks,” Advances in Artificial Neural Systems, vol. 2016, Article ID 2303181, 11 pages, 2016. https://doi.org/10.1155/2016/2303181.