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Multi-stage safe ship control with neural state constraints

机译:具有神经状态约束的多级安全船舶控制

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The paper describes an application of the dynamic programming method to determine own ship's safe trajectory during the passing of other ships encountered. A dynamic model of the process with kinematic constraints of state determined by a three-layer artificial neural network has been used for the synthesis of control. Non-linear activation functions in the first and second layers may be characterised by a tangent curve while the output layer is of a sigmoidal nature. Neural Network Toolbox from the Matlab package has been used to model the network. The learning process used an algorithm of a backward propagation of the error with adaptively selected learning step. The considerations have been illustrated with example of computer simulation the algorithm of determination the safe ship's trajectory in situation of passing 18 ships encountered, recorded on the ship's screen radar in real navigational situation in Skagerrak Strait.
机译:本文介绍了动态编程方法的应用,以确定在遇到的其他船舶的过程中确定自己的船舶安全轨迹。通过三层人工神经网络确定的动态约束的过程的动态模型已经用于控制的合成。第一和第二层中的非线性激活功能可以通过切线曲线表征,而输出层具有矩形性质。来自MATLAB包的神经网络工具箱已被用于模拟网络。学习过程使用了具有自适应选择的学习步骤的错误的向后传播的算法。考虑因素已经用计算机仿真示例进行了示例,该算法在遇到的18艘船的情况下,在船舶的屏幕雷达中记录了Skagerrak海峡的真正导航情况中的情况。

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