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首页> 外文会议>World Multiconference on Systemics, Cybernetics and Informatics(SCI 2002) v.6: Industrial Systems and Engineering I; 20020714-20020718; Orlando,FL; US >An artificial neural network that allows a robot submarine to learn to dive and adapt to change
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An artificial neural network that allows a robot submarine to learn to dive and adapt to change

机译：人工神经网络，可让机器人潜艇学习潜水并适应变化

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摘要

We show a very simple artificial neural network, namely an adaline that is able to learn to imitate very accurately the behavior of a complex non-linear dynamical system, such as a small submarine. The key to its success is the use of judiciously selected non-linear inputs for it. Such an adaline can thus provide the small submarine with an internal model of itself, which it can then use to calculate the signals that control its motion. The well-known ability of the adaline to adapt rapidly to changes endows the submarine with all the adaptive features that can be wished for in artificial systems as well as in biological systems. It learns very rapidly, by itself, to control its motion and, as we show in this article, once it has learned to dive, it performs flawlessly and can further adapt to any change in its environment or in itself. In particular, we show that it can very quickly learn to compensate for changes in the currents, the viscosity of the waters in which it moves, its own mass, its buoyancy and the maximum angle of deflection of its fins.
机译：我们展示了一个非常简单的人工神经网络，即一种adaline，它能够学习以非常精确的方式模仿复杂的非线性动力系统（例如小型潜艇）的行为。它成功的关键是为其使用经过明智选择的非线性输入。因此，这种柔韧性可以为小型潜艇提供其自身的内部模型，然后可以将其用于计算控制其运动的信号。柔韧性的众所周知的快速适应变化的能力使潜艇具有在人工系统以及生物系统中都希望具有的所有适应性特征。它本身可以非常迅速地学习以控制其运动，并且正如我们在本文中所展示的那样，一旦它学会了潜水，它就可以完美表现，并且可以进一步适应其环境或自身的任何变化。特别是，我们显示出它可以非常迅速地学习以补偿水流的变化，它在其中流动的水的粘度，其自身的质量，其浮力以及其鳍片的最大偏转角。

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