Ship Performance Research Based on BP Neural Network

Han Liu; Fang Zhen Song; Ming Ming Li; Bo Song

doi:10.4028/www.scientific.net/AMM.709.176

Paper Titles

Tailoring the Fundamental Frequency of Laminated Composite Panels Using Material Properties
p.157

Surface Fitting and Finite Element Inverse Analysis of Residual Stress in a Weldment by the Contour Method
p.162

Dynamics Analysis of Orientation Distribution of Fiber Suspensions in Shear-Extensional Flow
p.168

A Method Combined with the Genetic Algorithm for Design of High Efficiency Propeller
p.172

Ship Performance Research Based on BP Neural Network
p.176

Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine
p.180

Comparative Analysis of 3D Viscoelastic and Poroelastic Dynamic Boundary-Element Modelling
p.186

Finite Element Simulation of Stability and Resistance to Extreme Loads of Braced Shell with Elastomeric Bearings
p.190

Numerical Simulation Method of Leakage for Lip Seal
p.196

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 709Ship Performance Research Based on BP Neural...

Ship Performance Research Based on BP Neural Network

Abstract:

The problem is solved that it is hard to provide analysis formulas about the maximum equivalent stress, the maximum shear stress and the structural geometric parameters for a ship. The finite element calculation is done with orthogonal experimental design under the most dangerous case. The data obtained are used as the training and test samples to establish BP neural network models of ship’s maximum equivalent stress and maximum shear stress. With the aid of Neural network toolbox in MATLAB, the topological structure of BP neural network mapping relationship between the whole ship performance indexes and design variables is established. The training and testing are completed with the data tested by the shipyard and the correctness of this network is verified. The neural network required for further optimization design is obtained. The neural network is helpful in reducing the ship mass without exceeding the allowable stress.

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Periodical:

Applied Mechanics and Materials (Volume 709)

Pages:

176-179

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.709.176

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Online since:

December 2014

Authors:

Han Liu, Fang Zhen Song*, Ming Ming Li, Bo Song

Keywords:

BP Neural Network (BPNN), Frequency, Ship Carrier, Stress

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* - Corresponding Author

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