神经网络在自适应船舶操作系统中的应用

Neural network application to adaptive ship operation system

首先在RBF-Elman神经网络结构基础上,设计带输出反馈RBF-Elman递归神经网络,通过实验验证该算法具有收敛速度快、自适应能力强、自学习能力以及固有的非线性等特点。最后将利用带输出反馈RBFElman递归神经网络作为分类器进行船舶运动状态的辨别,实验结果表明此算法高效、可行。

First, this article design with output feedback RBF-EIman recurrent neural network on the basis of RBF-Elman neural network structure. Experiments show that the algorithm has a fast convergence, strong adaptability and inherent nonlinear characteristics. Finally, use tape output feedback RBF-Elman recurrent neural network as a classifier to identify the state of the ship motion. Experimental results show that this algorithm is efficient and feasible.