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

Subject Areas : Data Envelopment Analysis

معصومه  عباسی ¹ (گروه ریاضی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران)
عباس  قماشی ² (گروه ریاضی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران)

Received: 2017-11-20 Accepted : 2018-04-24 Published : 2019-06-01

Keywords: Stability, Data envelopment analysis, CCR, Recurrent neural network, Gradient method, Global convergence,

Abstract :

In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has a single-layer structure. A numerical example shows that the proposed model is effective to solve CCR model in DEA.

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