Model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzy model identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input–output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi–Sugeno fuzzy model is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzy model identification requires fewer fuzzy rules and a shorter tuning time.
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October 2011
Technical Briefs
Fuzzy Model Identification With Enhanced Validity Criterion for Mechanical System Design
Kuo-Ho Su
e-mail: sgh@faculty.pccu.edu.tw
Kuo-Ho Su
Graduate Institute of Digital Mechatronic Technology, Chinese Culture University
, 55, Haw-Kang Rd., Yang-Ming-Shan, Taipei 11114, Taiwan, R.O.C.
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Kuo-Ho Su
Graduate Institute of Digital Mechatronic Technology, Chinese Culture University
, 55, Haw-Kang Rd., Yang-Ming-Shan, Taipei 11114, Taiwan, R.O.C.e-mail: sgh@faculty.pccu.edu.tw
J. Mech. Des. Oct 2011, 133(10): 104501 (7 pages)
Published Online: September 27, 2011
Article history
Received:
December 31, 2010
Accepted:
May 12, 2011
Published:
September 27, 2011
Citation
Su, K. (September 27, 2011). "Fuzzy Model Identification With Enhanced Validity Criterion for Mechanical System Design." ASME. J. Mech. Des. October 2011; 133(10): 104501. https://doi.org/10.1115/1.4004483
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