模糊系统建模的工业应用

Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296) Pub Date : 1999-07-01 DOI:10.1109/IPMM.1999.792469

I. Turksen

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引用次数: 0

摘要

如果从系统数据库中获得历史系统行为数据，则可以用模糊数据挖掘方法建立工业系统的总体行为模型。给定输入输出数据向量，可以使用统一的系统建模方法利用模糊技术提取系统行为的“隐藏规则”。其中，模糊聚类分析可以与无监督学习相结合，提取模糊集隶属函数和模糊规则结构。基于最小误差准则的监督学习与参数推理相结合的方法可以确定组合算子。这消除了在执行近似推理算法时所需要的任意选择t规范和t一致性。给出的例子包括以最小延迟和最小混合等级钢产量为标准的连铸机炼钢调度。该方法也可应用于实验数据的药理学分析。

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Industrial applications of fuzzy system modeling

Aggregate industrial system behaviour models can be built with fuzzy data mining provided the historical system behaviour data are available from system databases. Given the input-output data vectors, a unified system modeling approach can be used to extract "hidden rules" of system behaviour using fuzzy technology. In particular, fuzzy cluster analysis could be used with unsupervised learning to extract fuzzy set membership function and the fuzzy rule structures. A parametric reasoning method combined with supervised learning with minimum error criteria could determine combination operators. This eliminates the arbitrary choice of t-norms and t-conorms that are required in the execution of approximate reasoning algorithms. Examples given include continuous caster scheduling in steel making with criteria of minimum tardiness and minimum mixed grade steel production. This methodology can also be applied to pharmacological analysis of experimental data.

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Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296)

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