Lin Feifan, Zhou Xiaojun, Li Chaojie, Huang Tingwen, Yang Chunhua
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):5322-5331. doi: 10.1109/TNNLS.2022.3186475. Epub 2023 Sep 1.
Many actual industrial production processes are dynamic and uncertain. When uncertain information are described by subjective experience and experts' knowledge based on scanty or vague information, fuzzy uncertainty exists. Fuzzy chance-constrained dynamic programming are applicable to industrial production modeling accompanied by fuzzy uncertainty and dynamics, where constraints need not or cannot be completely satisfied. In this article, a fuzzy chance-constrained dynamic optimization (FCCDO) formulation on the basis of credibility theory is established, in which, the credibility is used to measure the fuzzy uncertainty level of constraints. To solve the FCCDO problem (FCCDOP), an improved fuzzy simulation technique based on Hammersley sequence sampling is raised to transform fuzzy chance constraints to their deterministic equivalents, and then a data-driven state transition algorithm (DDSTA) using deep neural networks (DNNs) is put forward to achieve a stable, global and robust optimization performance. Finally, the successful applications of the FCCDO method to industrial studies demonstrate its advantages.
许多实际工业生产过程是动态且不确定的。当基于稀少或模糊信息通过主观经验和专家知识来描述不确定信息时，就存在模糊不确定性。模糊机会约束动态规划适用于伴有模糊不确定性和动态性的工业生产建模，其中约束不必或无法完全满足。本文基于可信性理论建立了模糊机会约束动态优化（FCCDO）公式，其中，可信性用于衡量约束的模糊不确定性水平。为解决FCCDO问题（FCCDOP），提出了一种基于哈默斯利序列采样的改进模糊模拟技术，将模糊机会约束转化为其确定性等价形式，然后提出一种使用深度神经网络（DNN）的数据驱动状态转移算法（DDSTA），以实现稳定、全局且鲁棒的优化性能。最后，FCCDO方法在工业研究中的成功应用证明了其优势。
