基于支持向量机元模型的随机鲁棒设计(英文)

Probabilistic Robustness Design Using Support Vector Machine based Metamodel

随机鲁棒设计是一种基于蒙特卡洛仿真的优化设计方法。通常情况下,对于复杂仿真模型的随机鲁棒设计时间开销很大。为减小随机鲁棒设计过程中的时间开销,使用参数最优的最小二乘支持向量机替代仿真模型进行随机鲁棒设计。使用标准粒子群优化算法搜索支持向量机参数和控制器参数的寻优。通过一个基准测试问题证明了该方法的可行性。

The probabilistic robustness design is a simulation-based design approach in nature. It is computationally intensive, sometimes even impossible, to perform probabilistic robustness design method on complex time-consuming simulation models. The least squares support vector machine based metamodel is introduced into probabilistic robustness design in order to alleviate the computational burden. Standard particle swarm optimization is employed in two aspects.＂ parameter optimization of the support vector machine metamodel and exploration of the controller parameter space for probabilistic robust solution. An application to a benchmark problem is displayed to demonstrate the feasibility of the proposed method .