1) ELECTRE algorithm
ELECTRE算法
1.
In view of the problem of high rate of large fragmentation in long-hole blasting at Wulongquan Limestone Quarry and based on the statistic data of blasting tests on various schemes, the comprehensive sequencing for the optimized selection of blasting scheme was studied, in which the exponential function standardization method and ELECTRE algorithm model were introduced.
引入了指数函数标准化方法和ELECTRE算法模型,选取最优方案,生产实际中取得良好效果。
2.
Moreover,the Electre Algorithm is applied to the process of supplier selection and evaluation.
分析了反应型供应链的特点,建立了反应型供应链供应商的评价指标体系,并将改进的Electre算法引入到供应商的评价与选择过程当中。
2) improved ELECTRE-Ⅰalgorithms
改进ELECTRE-Ⅰ算法
4) ELECTRE-I
ELECTRE-I法
1.
Study on Optimum Selection in Project Bidding Decision-making Based on ELECTRE-I;
基于ELECTRE-I法的施工项目投标优选分析
5) simplified ELECTRE method
简化ELECTRE法
1.
The application of simplified ELECTRE method based on combination weighting in reinforcement scheme optimization for bridges was investigated in order to select the optimal scheme from several primarily feasible ones.
为了从众多初步可行的桥梁加固方案中选择最佳方案,研究了组合赋权简化ELECTRE法在桥梁加固方案优选中的应用问题。
6) ELECTRE method
ELECTRE方法
1.
As an important method in MADM, the ELECTRE method has also played an extensive and successful role in agriculture, energy, environment, finance, project management, and other fields.
作为多属性决策中的一种重要方法，ELECTRE方法自提出以来也在农业、能源、环境、金融、项目管理等领域获得了广泛而成功的应用。
2.
Combining the concepts of fuzzy theory,this paper extends the ELECTRE method for fuzzy multiattribute group decision making(FMAGDM),which is an important part of decision support system.
结合模糊集理论知识,将经典的ELECTRE方法拓展到模糊环境下,用来解决模糊多属性群决策问题。
补充资料：BP算法
分子式：
CAS号：
性质：又称逆推学习算法，简称BP算法，是1986年鲁梅哈特(D. E. Rumelhart)和麦克莱朗德(J. L. McClelland)提出来的。用样本数据训练人工神经网络(一种模仿人脑的信息处理系统)，它自动地将实际输出值和期望值进行比较，得到误差信号，再根据误差信号从后(输出层)向前(输入层)逐层反传，调节各神经层神经元之间的连接权重，直至误差减至满足要求为止。反向传播算法的主要特征是中间层能对输出层反传过来的误差进行学习。这种算法不能保证训练期间实现全局误差最小，但可以实现局部误差最小。BP算法在图像处理、语音处理、优化等领域得到应用。
CAS号：
性质：又称逆推学习算法，简称BP算法，是1986年鲁梅哈特(D. E. Rumelhart)和麦克莱朗德(J. L. McClelland)提出来的。用样本数据训练人工神经网络(一种模仿人脑的信息处理系统)，它自动地将实际输出值和期望值进行比较，得到误差信号，再根据误差信号从后(输出层)向前(输入层)逐层反传，调节各神经层神经元之间的连接权重，直至误差减至满足要求为止。反向传播算法的主要特征是中间层能对输出层反传过来的误差进行学习。这种算法不能保证训练期间实现全局误差最小，但可以实现局部误差最小。BP算法在图像处理、语音处理、优化等领域得到应用。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条