A Recurrent Neural Network Model for solving BCC Model in Data Envelopment Analysis

In this paper we present a recurrent neural network model for solving BCC Modelin 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 oflyapunov 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.

کلیدواژه های A Recurrent Neural Network Model for solving BCC Model in Data Envelopment Analysis:

Recurrent neural network ، Gradient method ، Data envelopment analysis ، Efficient DMU ، Stability ، Global convergence