ANAFIS and neural network for modeling and prediction of ship squat in shallow waters: a new approach

• Autorzy: Zeraatgar H. , Akbari Vakilabadi K. , Salmalian K. , Haghi A. K.
• Języki publikacji: EN

Abstrakty

EN

The reduction of the distance between ship floor and seabed, while the ship is moving forward, is called squat. In this research, squat is determined for vessels with Series-60 hull forms in various depths by experimental methods and then different numerical methods are employed for squat modeling. For this reason, a set of facilities for testing the ship movement in shallow waters is prepared. A series of models of the vessel is manufactured and many tests are carried out. The aim of the present study is to demonstrate the usefulness of an adaptive-network-based fuzzy inference system (ANFIS) for modeling and predicting the squat parameter for ships in shallow waters. It is also shown how dimensionless squat (S*) varies with the variation of important parameters, namely: block coefficient (CB), dimensionless distance between the seabed and ship floor […] and hydraulic Froude Number (Fnh). The results obtained through the ANFIS are also compared with those of a multiple linear regression and GMDH-type neural network with multi-layered feed forward back propagation algorithm. The results show that the ANFIS-based squat has higher predictability function than other numerical methods.

Słowa kluczowe

EN

ANFIS; neural network; squat; shallow water; physical model

PL

sieci neuronowe; model fizyczny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mechanics and Engineering
• Rocznik: 2011
• Tom: Vol. 16, no 1
• Strony: 211--231
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Zeraatgar H.

autor

Akbari Vakilabadi K.

autor

Salmalian K.

autor

Haghi A. K.

• AmirKabir University of Technology Tehran, IRAN,

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