Fuzzy modeling of predictionMs temperature for martensitic stainless steel

A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting Ms temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fizzy clustering algorithm. Kalman filtering was used to identify...

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Bibliographic Details
Published in:Journal of Wuhan University of Technology. Materials science edition 2004-01, Vol.19 (4), p.106-109
Main Authors: Jiang, Yue, Zhong-da Yin, Peng-chao, Kang, Liu, Yong
Format: Article
Language:English
Subjects:
Algorithms
Chemical composition
Chromium
Clustering
Kalman filters
Martensitic stainless steels
Modelling
Molybdenum
Parameter identification
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Summary:A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting Ms temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fizzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Only Grade 95 steel are available for training and validation, and the fuzzy model is valid for the following element concentration ranges (wt%): 0.01
ISSN:1000-2413
1993-0437
DOI:10.1007/BF02841383