Reliability-Based Design Optimization With the RBF Neural Network Model
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摘要: 在结构构件尺寸、材料属性以及外部载荷等不确定性因素影响下,基于可靠度的优化给出了兼顾结构的成本和安全性能的安全设计方案.由于传统的可靠度优化方法采用嵌套的双层优化列式求解,因此导致计算量过大.为了克服这个问题,学者们相继提出了解耦方法和单循环方法等方法.该文采用RBF神经网络模型用于可靠度优化问题的求解中,通过拉丁超立方方法构造代理模型,并用误差指标来验证代理模型的精确程度,同时自适应更新代理模型直至满足需求.通过与现有可靠度优化4种主流算法的比较,说明了该文提出算法的高效性和稳健性.Abstract: The reliability-based design optimization (RBDO) provides a trade-off between cost and safety given a lot of uncertain factors, such as structure sizes, material properties and external loads. The traditional RBDO approaches were computationally expensive owing to the nesting bilevel optimization. Thereafter, the decoupling approach and single loop approach were proposed to overcome this obstruction. However,the computational demand of these 2 types of approaches was still too huge for complex engineering problems. Here a new adaptive RBDO approach was presented based on the RBF neural network theory. The latin hypercube sampling method was used to construct the surrogate model. The error metrics were used to validate the RBF neural network model and the surrogate model was updated adaptively. Compared with the previous 4 popular RBDO algorithms, the presented method is efficient and robust.
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Key words:
- reliability /
- optimization /
- RBF neural network /
- efficiency /
- robustness
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