Fuzzy Regression and Interpolation with Application To Fuzzy Reliability

建议的研究是发展一种基于模糊集的方法 利用不确定和不精确数据的理论 回归和插值，进一步应用于可靠性 分析. 具体而言，模糊回归建模的情况下， 对称和非对称隶属函数都将 使用各种回归性能指标进行研究，适用于 线性和非线性回归。 模糊插值技术将 使用样条插值法和克里金法进行开发。 的结果 将提供拟议的方法学发展、准则 在技术上正确使用模糊集和贝叶斯技术， 包括危险废物处理设施的设计问题和 结构和地质环境系统的可靠性评估。 本项目与I的ECS-8802350提案合作。 博加迪 这两个PI都是著名的模糊集研究，以及 有资格进行这项研究。 他们各自的机构， 亚利桑那州和内布拉斯加州提供了比适合的环境， 工作 建议88财政年度的赔偿额为29，841美元， 2018年全年30,472元。

The proposed research is to develop a methodology based on fuzzy set theory for the utilization of uncertain and imprecise data in regression and interpolation, with further application to reliability analysis. Specifically, fuzzy regression modeling in the cases of both symmetric and non-symmetric membership functions will be investigated using various regression performance measures, applied to linear and nonlinear regression. Fuzzy interpolation techniques will be developed using spline interpolation and kriging. As a result of the proposed methodological developments, guidelines will be provided for the proper use of fuzzy set and Bayesian techniques in technical areas including design issues in hazardous waste facilities and the reliability assessment of structures and geo-environmental systems. This project is in cooperation with proposal ECS-8802350 by I. Bogardi. Both PIs are renowned in fuzzy set research and well qualified to conduct the research. Their respective institutions, Arizona and Nebraska provide more than suitable environments for the work. An award at the level of $29,841 is recommended for FY88 with a continuation for $30,472 for FY89.