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混沌对角递归神经网络的船舶横摇预报方法

, PP. 1681-1684

Keywords: 对角递归神经网络,混沌,动量梯度学习算法,船舶横摇预测,前向神经网络

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Abstract:

船舶运动在一定条件下会出现混沌特性,因此可以利用混沌神经网络对其进行预报.对传统的混沌对角递归神经网络模型各权值的训练进行优化,给出了基于Lyapunov函数的各层权所通用的学习速率调整算法的收敛定理并加以证明.仿真结果表明,采用优化采样时刻??可提高各权值的精确度,使收敛性得到改善,能有效提高预报精度和延长预报时间.与前向神经网络BP预测相对比,优化后的模型具有很好的预测效果.

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