Title of article :
A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis
Author/Authors :
Ghomashi, A Department of Mathematics - Science and Research Branch Islamic Azad University , Jahanshahloo, G.R Department of Mathematics - Science and Research Branch Islamic Azad University , Hosseinzadeh Lotfi, F Department of Mathematics - Science and Research Branch Islamic Azad University
Abstract :
In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is
shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-layer structure. Simulation shows that the proposed model is effective to identify efficient DMUs in DEA.
Keywords :
Global convergence , Stability , Efficient DMU , Data envelopment analysis , Gradient method , Recurrent neural network
Journal title :
Astroparticle Physics
