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

Iranian Journal of Optimization

مقاله 1، دوره 11، شماره 1، شهریور 2019، صفحه 1-7   

نوع مقاله: Research Paper

نویسندگان

Masomeh Abbasi^{* 1}؛ Abbas Ghomashi²

¹Department of Mathematics,Kermanshah Branch, Islamic Azad University, Kermanshah,Iran

²Department of Mathematics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

چکیده

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.

کلیدواژه‌ها

Recurrent neural network؛ Gradient method؛ Data envelopment analysis؛ CCR؛ Stability؛ Global convergence

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