Title :
Neuro-fuzzy classification for the job assignment problem
Author :
Kelemen, Arpad ; Kozma, Robert ; Liang, Yulan
Author_Institution :
Dept. of Math. Sci., Univ. of Memphis, TN, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
We propose to use an adaptive neuro-fuzzy inference system (ANFIS) in order to optimize decision making for the job assignment problem of the US Navy. Results need to be considered carefully because of the high noise level naturally present in the data coming from human decisions. We describe how the data was acquired and preprocessed. The design issues of the ANFIS are discussed
Keywords :
adaptive systems; data acquisition; fuzzy neural nets; human resource management; inference mechanisms; military computing; pattern classification; US Navy; adaptive system; data acquisition; decision making; job assignment problem; neural fuzzy inference system; rule extraction; Adaptive systems; Automation; Computer architecture; Decision making; Humans; Intelligent agent; Job production systems; Noise level; Personnel; Resource management;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007797
