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来源：百度知道 编辑：UC知道 时间：2024/09/21 12:39:48
Abstract
The widely used bridge management systems require appropriate preliminary deterioration diagnosis and modeling. This paper presents a fuzzy rule-based inference system for bridge damage diagnosis and prediction which aims to provide bridge designers with valuable information about the impacts of design factors on bridge deterioration. The validity of these influence parameters is verified by an input variable identification method. Fuzzy logic is utilized to handle uncertainties and imprecision involved. A modified mountain clustering method is employed to create the training data set. A fuzzy partitioning algorithm is implemented to construct the membership functions of the input variables and to induce the fuzzy rules from the numerical data. The generated rule base is checked and optimized based on the similarity measures among the input fuzzy sets. Illustrative examples show that the system has a high classification accuracy rate with a small number of rules.
The widely used bridge management systems require appropriate preliminary deterioration diagnosis and modeling. This paper presents a fuzzy rule-based inference system for bridge damage diagnosis and prediction which aims to provide bridge designers with valuable information about the impacts of design factors on bridge deterioration. The validity of these influence parameters is verified by an input variable identification method. Fuzzy logic is utilized to handle uncertainties and imprecision involved. A modified mountain clustering method is employed to create the training data set. A fuzzy partitioning algorithm is implemented to construct the membership functions of the input variables and to induce the fuzzy rules from the numerical data. The generated rule base is checked and optimized based on the similarity measures among the input fuzzy sets. Illustrative examples show that the system has a high classification accuracy rate with a small number of rules.
摘要
目前广泛运用的桥梁管理系统需要合适的损害预期分析和建模。本文介绍了一种基于模糊理论的推论系统，用于桥梁损伤分析和预报，从而为设计者提供关于设计因素对桥梁损伤的有效信息。通过变量确认来保证参数的有效性。用模糊论来控制相关的不确定和不准确因素。用一种山峰聚类算法来建立一系列校准数据. 模糊分块算法用来建立输入变量的成员函数，从而通过数值计算确立模糊法则。由此生成的模糊法则再由所输入的模糊条件进行检查和优化。所列算例很好的说明，此系统在规则较少的情况下，可以达到较高的分类准确率。
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摘要
广泛使用的桥梁管理系统，需要适当的初步诊断和建模恶化。本文提出了一种模糊规则为基础的推理系统的桥梁损伤诊断和预测的目的是桥梁设计师提供了宝贵的信息的影响，设计因素对桥梁恶化。的有效性，这些影响参数，验证了一个输入变量识别方法。模糊逻辑用于处理不确定性和不准确的。改良山区聚类方法是创造就业的培训数据集。模糊分割算法是实施建设的隶属函数的输入变量，并促使模糊规则的数值数据。规则库的生成和优化检查的基础上的相似性措施中的输入模糊集。示例表明，该系统具有较高的分类准确率少数规则。