李清明, 唐辉明. 2015: 工程地质环境质量评价中两难困境问题的一种解决方法. 工程地质学报, 23(s1): 778-785. DOI: 10.13544/j.cnki.jeg.2015.s1.120
    引用本文: 李清明, 唐辉明. 2015: 工程地质环境质量评价中两难困境问题的一种解决方法. 工程地质学报, 23(s1): 778-785. DOI: 10.13544/j.cnki.jeg.2015.s1.120
    LI Qingming, TANG Huiming. 2015: A SOLUTION TO THE DILEMMA PROBLEM IN THE VALUATION OF ENGINEERING GEOLOGICAL ENVIRONMENTAL QUALITY. JOURNAL OF ENGINEERING GEOLOGY, 23(s1): 778-785. DOI: 10.13544/j.cnki.jeg.2015.s1.120
    Citation: LI Qingming, TANG Huiming. 2015: A SOLUTION TO THE DILEMMA PROBLEM IN THE VALUATION OF ENGINEERING GEOLOGICAL ENVIRONMENTAL QUALITY. JOURNAL OF ENGINEERING GEOLOGY, 23(s1): 778-785. DOI: 10.13544/j.cnki.jeg.2015.s1.120

    工程地质环境质量评价中两难困境问题的一种解决方法

    A SOLUTION TO THE DILEMMA PROBLEM IN THE VALUATION OF ENGINEERING GEOLOGICAL ENVIRONMENTAL QUALITY

    • 摘要: 在工程地质环境质量评价中, 传统的综合指数法、模糊综合评价法和灰色聚类法等评价方法的缺点是存在因素权重的取值困境问题, 而人工神经网络评价方法的缺点是存在训练样本和检测样本的选择困境问题。本文对一个工程地质环境系统的单元进行了分类, 提出了敏感单元、不敏感单元和类不敏感单元的概念, 并定义不敏感单元为单元质量类别不随因素权重组合变化而发生改变的单元。在不敏感单元概念的基础上, 定义标准训练集为由不敏感单元的输入-输出对组成的集合, 进而提出了一种基于标准训练集的人工神经网络方法, 为解决工程地质环境质量评价中的两难困境问题提供了一种解决方法。

       

      Abstract: In the valuation of engineering geological environmental quality, there is the weight dilemma problem in the traditional weighted composite index method, fuzzy comprehensive evaluation method, grey clustering method and etc., however, there is the dilemma problem of selecting training samples and testing samples in the artificial neural network method in this paper, sensitive unit, insensitive unit and similar-insensitive unit are firstly put forward according to the classification of the units of an engineering geological environmental system. An insensitive unit is defined as the unit whose quality category is constant while the maximum ratios of factor weights change from K=1~K=.Based on the concept of insensitive unit, the standard training set is defined as the input-output pairs of insensitive units. The paper has proposed a new artificial neural network method based on standard training set, trying to solve the weight dilemma problem in the traditional evaluation method and the dilemma problem of selecting training sample and testing sample in the artificial neural network method.

       

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