The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
Transactions ID Number: 20-650
Full Name: Kaveh Salmalian
Position: Professor
Age: ON
Sex: Male
Address: P.O Box 44715-1333, DEPARTMENT OF MECHANICAL ENGINEERING, Islamic Azad University, Langaroud Branch, Langaroud, Guilan, Iran
Country: IRAN
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E-mail address: salmalian.kaveh@gmail.com
Other E-mails: kavehsalmalian@iaul.ac.ir
Title of the Paper: anfis and neural network for modeling and prediction of ship squat in shallow waters
Authors as they appear in the Paper: K.Salmalian, M.Soleimani
Email addresses of all the authors: majidsoleimani@iau-lahijan.ac.ir
Number of paper pages: 9
Abstract: Squat is defined as the increase of draught of vessel due to its forward movement in shallow water. In this paper the squat parameter is established for Series-60 hull forms vessels in different depths via experimental methods and afterward diverse numerical methods are utilized to model squat. So, some facilities for the ship movement testing in shallow waters are organized. A series of models of the vessel is manufactured and numerous tests are performed attentively. In the present work, capability of the Adaptive-network-based fuzzy inference system (ANFIS) in modeling and predicting squat parameter for ships in shallow waters is demonstrated well. In addition, It is also extracted the mathematical relations between dimensionless squat ( ) and significant variables namely, block coefficient (CB), dimensionless distance between the seabed and ship floor ( ) and hydraulic Froude Number . Finally, the obtained results of ANFIS modeling are compared with those of a !
multiple linear regression and GMDH-type neural network. The consequences confirm that the ANFIS-based squat has higher predictability function than other employed methods.
Keywords: ANFIS, GMDH, Squat, Shallow Water, Physical Model
EXTENSION of the file: .doc
Special (Invited) Session: Modeling and Prediction of Ship Squat Using ANFIS and GMDH-type Neural Network
Organizer of the Session: 202-259
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