TY - CHAP
T1 - An Application of Fuzzy Modeling to Analysis of Rowing Boat Speed
AU - Tachibana, Kanta
AU - Furuhashi, Takeshi
AU - Shimoda, Manabu
AU - Kawakami, Yasuo
AU - Fukunaga, Tetsuo
N1 - Publisher Copyright:
© 2001 by Taylor & Francis Group, LLC.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Fuzzy modeling has distinct features, which are applicability to nonlinear systems and ability to extract knowledge. Fuzzy neura!.etwork (FNN) enables automatie acquisition of knowledge. The authors have proposed an uneven division of input space for the FNN which reduces the number of fuzzy rules without sacrificing the precision of the model. In many sports, nonlinear factors affect the performance. In rowing competitions, the performance criterion is the boat speed. In this chapter, fuzzy modeling is applied to reveal the relationships between the supplied power and the boat speed. The forces and the angles of on-water rowing are measured. The subjects are candidates of Japanese national team rowers. The total propulsive work, consistency and uniformity of the propulsive power were calculated from the force and the angle data. The relationships between these factors and the performance were identified with fuzzy modeling. Compared to linear regression, a more precise and simpler model was obtained.
AB - Fuzzy modeling has distinct features, which are applicability to nonlinear systems and ability to extract knowledge. Fuzzy neura!.etwork (FNN) enables automatie acquisition of knowledge. The authors have proposed an uneven division of input space for the FNN which reduces the number of fuzzy rules without sacrificing the precision of the model. In many sports, nonlinear factors affect the performance. In rowing competitions, the performance criterion is the boat speed. In this chapter, fuzzy modeling is applied to reveal the relationships between the supplied power and the boat speed. The forces and the angles of on-water rowing are measured. The subjects are candidates of Japanese national team rowers. The total propulsive work, consistency and uniformity of the propulsive power were calculated from the force and the angle data. The relationships between these factors and the performance were identified with fuzzy modeling. Compared to linear regression, a more precise and simpler model was obtained.
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U2 - 10.1201/9780429177453-7
DO - 10.1201/9780429177453-7
M3 - Chapter
AN - SCOPUS:85196599711
SN - 9780849322693
SP - 223
EP - 240
BT - Fulzy Learning and Applications
PB - CRC Press
ER -