Optimization Research of Ship Parameters for FRP Fishing Vessels Based on GRNN and Genetic Algorithm
Optimization Research of Ship Parameters for FRP Fishing Vessels Based on GRNN and Genetic Algorithm
复制标题
基于GRNN和遗传算法的玻璃钢渔船船舶参数优化研究
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Chen Ming
中科院分区：
文献类型：
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作者：
Chen Ming
利用神经网络的非线性拟合能力提高模型的拟合精度，建立了基于广义回归神经网络(GRNN)的玻璃钢渔船“船型参数-船阻”数学模型，同时利用改进的遗传算法(GA)的非线性优化能力完成了船型参数的优化设计，优化结果可为玻璃钢渔船的初步设计提供参考。
The mathematical model of "ship parameters-ship resistance" for FRP fishing vessels,based on generalized regression neural networks(GRNN),is established by using the nonlinear fitting ability of neural networks to improve the model fitting precision.At the meantime,the ship parameters' optimizing design is accomplished by the improved genetic algorithm(GA) with its nonlinear optimization ability.The optimized results can be used as the reference for the preliminary design of FRP fishing vessels.