基于g_λ变量的冗余优化模型  

Redundancy Optimization Models Based on g_λ Variable

作　　者：张鸿[1,2] 马丽涛[2] 哈明虎[1] 

机构地区：[1]河北工程大学水电学院,河北邯郸056038 [2]河北工程大学数理科学与工程学院,河北邯郸056038

出　　处：《模糊系统与数学》2017年第1期128-135,共8页Fuzzy Systems and Mathematics

基　　金：国家自然科学基金资助项目(61073121);河北省应用基础研究计划重点基础研究项目(16964213D);河北省教育厅青年基金项目(QN2015116);邯郸市科学技术研究与发展计划项目(1528102058-5;1521109072-1);河北省2016年研究生创新能力资助项目(222)

摘　　要：为了解决Sugeno测度空间上系统寿命期望值、系统寿命的α乐观值和系统可靠性的系统性能优化问题,提出用g_λ变量来表征冗余元件寿命的冗余优化模型。首先,建立Sugeno冗余期望值模型、Sugeno冗余机会约束规划模型和Sugeno冗余相关机会规划模型;其次,为了求解模型,设计一种基于Sugeno模拟、多层神经网络和遗传算法的混合智能算法;最后,通过桥式系统的系统性能优化的算例验证所提模型和算法的可行性。In order to solve the problems of system performance optimization for mean system-lifetime, α-system lifetime and system reliability on Sugeno measure space, redundancy optimization models which use gλ variable to characterize redundancy component life are provided. Firstly, Sugeno redundancy expected value model, Sugeno redundancy chance-constrained programming model and Sugeno redundancy dependent chance programming model are given. Then a hybrid intelligent approach which consists of Sugeno simulation,multilayer perceptron network and genetic algorithm is presented to solve the optimization. Finally, the feasibility of the optimization approach and hybrid approach is shown by the redundancy optimization for the system performance of bridge system.

关 键 词：gλ变量 Sugeno测度空间 冗余优化 Sugeno模拟 混合智能算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]