Proceedings ArticleDOI
New system identification technique using fuzzy regression analysis
M. Kaneyoshi,H. Tanaka,M. Kamei,Hitoshi Furuta +3 more
- pp 528-533
TLDR
In this paper, a system identification method is developed, in which measured field data are assumed to be fuzzy data and fuzzy regression analysis is applied to the process of system identification, which can be solved without difficulty by using a linear programming algorithm.Abstract:
A system identification method is developed, in which measured field data are assumed to be fuzzy data and fuzzy regression analysis is applied to the process of system identification. Although the method includes fuzzy coefficients in the formulation, it can be solved without difficulty by using a linear programming algorithm. This fuzzy system identification method has been applied to the construction of a cable-stayed bridge, the Shugahara-Shirokita Bridge in Osaka, Japan. The results confirm that the system identification technique proposed is not only simple to handle but also very practical, compared with previous methods. >read more
Citations
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Journal ArticleDOI
Interval regression analysis by quadratic programming approach
Hideo Tanaka,H. Lee +1 more
TL;DR: The unified quadratic programming approach obtaining the possibility and necessity regression models simultaneously is proposed and polynomials are considered as regression models since any curve can be represented by the polynomial approximation.
Journal ArticleDOI
Support vector interval regression networks for interval regression analysis
TL;DR: The convergence rate of SVIRNs is faster than the conventional networks with BP learning algorithms or with robust BPlearning algorithms for interval regression analysis, and a traditional back-propagation (BP) learning algorithm can be used to adjust the initial structure networks of SVirNs under training data sets without or with outliers.
Journal ArticleDOI
Fuzzy Regression Analysis by Support Vector Learning Approach
Pei-Yi Hao,Jung-Hsien Chiang +1 more
TL;DR: This paper incorporates the concept of fuzzy set theory into the support vector regression machine and can achieve automatic accuracy control in the fuzzy regression analysis task.
Book ChapterDOI
Fuzzy regression analysis
Phil Diamond,Hideo Tanaka +1 more
TL;DR: This chapter considers two types of fuzzy regression, the first is based on possibilistic concepts and the second upon a least squares approach, both of which reduce to linear programming.
Journal ArticleDOI
Moment of Inertia Identification Using the Time Average of the Product of Torque Reference Input and Motor Position
TL;DR: In this article, the authors proposed a moment of inertia identification algorithm for mechatronic servo systems with limited strokes, which utilizes periodic position reference input, and identifies the inertia of the servo system based on the time average of torque reference input and motor position.
References
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Journal ArticleDOI
Possibilistic linear systems and their application to the linear regression model
H. Tanaka,Junzo Watada +1 more
TL;DR: A new interpretation of fuzzy linear regression is presented and also includes a new method by which interval analysis can be done in fuzzy numbers.
Book ChapterDOI
Optimum Cable Tension Adjustment Using Fuzzy Regression Analysis
TL;DR: The authors have developed new methods to overcome problems through the use of the fuzzy set theory to determine the optimum cable pre-stresses in the design of cable-stayed bridges.
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