论文标题
实时优化符合贝叶斯的优化和无衍生化的优化：修饰符适应的故事
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
论文作者
论文摘要
本文研究了一类新的修饰剂适应方案，以在实时优化不确定过程中克服植物模型不匹配。主要贡献在于贝叶斯优化和无衍生化优化领域的概念的整合。拟议的方案嵌入了物理模型，并依靠信任区域的想法，以最大程度地降低探索过程中的风险，同时采用高斯过程回归以非参数方式捕获植物模型不匹配，并通过习惯功能来推动探索。在数值案例研究中说明了使用采集函数，了解过程噪声水平或指定名义过程模型的好处，包括半批次光生反应器优化问题。
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the areas of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-region ideas to minimize risk during the exploration, while employing Gaussian process regression to capture the plant-model mismatch in a non-parametric way and drive the exploration by means of acquisition functions. The benefits of using an acquisition function, knowing the process noise level, or specifying a nominal process model are illustrated on numerical case studies, including a semi-batch photobioreactor optimization problem.
