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基于变权重RBF组合模型的煤与瓦斯突出强度预测

ISSN号：1003-3033
期刊名称：《中国安全科学学报》
时间：0
分类：O225[理学—运筹学与控制论;理学—数学] TN915.01[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]College of Information Science and Technology, Bohai University, Jinzhou 121013, China
相关基金：supported by the National Natural Science Foundation of China （70971059）, the Humanity and Social Science Youth Foundation of Ministry of Education of China （12YJC870030）, the National Natural Science Foundation Project of China （60872042）, the Fundamental Research Funds for the Central Universities （2011RC0203）

关键词： QOS模型, WEB服务, 异构, 集体决策, 基础, 三角模糊数, TOPSIS, 服务质量, Web service, service selection, heterogeneous QoS, multi-periods QoS, group decision making

中文摘要：

在开、动态的因特网环境，服务(QoS ) 的多时期质量，基于异构的 QoS，模型和无常重量努力使万维网服务选择最佳的多重决策人。为了解决困难，上面的、动态万维网服务选择组决策基于异构的 QoS 模型(DWSSGD_HQM ) 被建议。绘画在上并且由类似为顺序偏爱扩大技术到理想的答案(TOPSIS ) ， DWSSGD_HQM 基于在实数，间隔数字，三角形的模糊数字和 intuitionistic 表示的异构的 QoS 模型支持多重决策人模糊分别地数，并且考虑多时期 QoS 和用户和组的重量。六个步骤被详细说明。他们正在变换进的模型在间隔数字表示了的异构的 QoS，计算加权的规范的决定矩阵，决定积极理想的组，计算，聚集并且否定理想的答案候选人靠近度为每个决策人的多时期靠近度，并且评价选择。最后，实验被使用实际 QoS 数据评估建议途径的有效性进行。

英文摘要：

In open and dynamic Internet environment, multi-periods quality of service （QoS）, multiple decision-makers based on heterogeneous QoS models and uncertainty weights make optimal Web service selection hard. To solve the difficulties above, dynamic Web service selection group decision-making based on heterogeneous QoS models （DWSSGD_HQM） is proposed. Drawing on and extending technique for order preference by similarity to ideal solution （TOPSIS）, DWSSGD_HQM supports multiple decision-makers based on heterogeneous QoS models expressed in real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers respectively, and takes multi-periods QoS and weights of user and group into account. Six steps are detailed. They are converting heterogeneous QoS models into ones expressed in interval numbers, calculating weighted normalized decision-matrix, determining the group positive-ideal and negative-ideal solutions, calculating the close-degrees of candidates, aggregating close-degrees of multi-periods for each decision-maker, and ranking the alternatives. Finally, experiments are conducted by using actual QoS data to evaluate the effectiveness of the proposed approach.

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