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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 system performance optimization problem of system life expectancy, system lifetime optimism and system reliability in Sugeno measure space, a redundancy optimization model is proposed to characterize the life span of redundant components with g_λ variables. First, Sugeno redundancy expectation model, Sugeno redundancy opportunistic constrained programming model and Sugeno redundancy-related opportunity planning model are established. Secondly, in order to solve the model, a hybrid intelligent algorithm based on Sugeno simulation, multilayer neural network and genetic algorithm is designed. Finally, the feasibility of the proposed model and algorithm is verified by an example of the system performance optimization of the bridge system.