| 摘要: |
| 提出了一种基于熵权多目标决策的逼近理想解法(TOPSIS)和人工神经网络(ANN)的炼油企业绩效评价方 法，以熵权TOPSIS的企业绩效评价结果作为学习样本，对神经网络进行训练、测试,进而对指标加以赋权，最终得到 了企业绩效综合评判式,并将其用于炼油企业绩效评价。实例分析结果表明，该方法科学有效、实际可行，具有一定 的智能性，为炼油企业绩效评价提供了一种新的途径。 |
| 关键词: 炼油企业 绩效评价 熵技术 多目标决策的逼近理想解法 人工神经网络 综合评价 |
| DOI： |
| 分类号: |
| 基金项目:山东省自然科学基金资助项目(Y2003H01) |
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| An approach based on entropy-weighted technique for order preference by similarity to ideal solution and artificial neural network for oil refining enterprises performance evaluation |
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SONG Jie-kun, ZHANG Zai-xu,ZHANG Xiao-hui
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(College of Economic Administration in China University of Petroleum，Dongying 257061，China)
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| Abstract: |
| An approach based on entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) method and artificial neural network (ANN) was proposed for oil refining enterprises performance evaluation. Using the results of entropy-weighted TOPSIS method as learning sample to train and test the artificial neural network, the weight of performance indicator and a synthetic evaluation formula were obtained. The oil refining enterprises performance evaluation was calculated by the formula. An example testifies the efficiency, practicability and intellectual ability of the method. |
| Key words: oil refining enterprises performance evaluation entropy technology technique for order preference by similarity to ideal solution method artificial neural network synthetic evaluation |
