Paper Info
Title
Decision-making modeling method based on artificial neural network and data envelopment analysis
Abstract
When people make use of the limited, expensive and historical data to build multiple-input and multiple-output nonlinear mathematical model for decision-making, they often face the problems whether or not all of the experimental data can be used directly for modeling, although artificial neural network (ANN) is a good method to describe the non-linear relationship between inputs and outputs. In the paper, decision-making modeling method based on feed forward ANN and data envelopment analysis (DEA) is brought forward. Experimental data were evaluated and projected by DEA, a widely used method to evaluate relative efficiency among decision making units (DMU). Then the experimental data would become more scientific and reasonable, and all of them could be used for decision-making modeling of ANN. Experiments show that the model of ANN, which gained by training these data, is DEA effective. So it is a new method for optimal data utilizing and decision-making modeling. The method is useful to the research, which may only get limited and high cost data after several times or several years of experiments.
Year
DOI
Venue
2004
10.1109/IGARSS.2004.1369783
IGARSS
Keywords
Field
DocType
geophysical techniques,geophysics computing,modeling method,decision making,decision-making,data acquisition,data envelopment analysis,artificial neural network,mimo,neural nets,relative efficiency,data envelope analysis,feed forward,mathematical model
Efficiency,Data mining,Nonlinear system,Experimental data,Computer science,Data acquisition,Data envelopment analysis,Artificial intelligence,Artificial neural network,Machine learning,Feed forward,Cost database
Conference
Volume
Issue
ISSN
4
null
2153-6996
ISBN
Citations 
PageRank 
0-7803-8742-2
1
0.40
References 
Authors
0
3
Name
Order
Citations
PageRank
Caicong Wu161.59
Xiuwan Chen23318.04
Yinsheng Yang311.41