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Backing Up a Truck and Trailer Uisng Sets of Three-Neuron Controllers

机译:使用一组三神经控制器备份卡车和拖车

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This work is a continuation of the effort reported in several previous papers presented at ANNIE Conferences (Alexander 1994) (Alexander and Bradley 1995) by the first author. A little-modeled property of biological neurons - the ability of each neuron to fire both above and below its average firing rate - is the feature which oru model exploits. The equations developed by the author in his dissertation model neurons possessing an average firing rate. In previous papers we had demonstrated the userfulness of this feature. Thus far, these equations have successfully maintained, at constant setpoint, the height of water in a tank under conditions of randomly changing inflow, and backed a truck to a loading dock. In this paper we demonstrate the ability of another sets of equations which, also employ the average firing rate concept, to successfully back a truck with a trailer attached to a loading dock. Our algorithm for determining the steering angle (for the cab's or truck's wheels) is considerably simpler than the well-known one given by Kosko (kosko 1992).
机译:这项工作是在Annie会议(Alexander 1994)(Alexander 1994)(Alexander 1994)提供的几篇上一篇论文中报告的努力延续了第一作者。生物神经元的一点建模性 - 每种神经元以上和低于其平均射击率的能力 - 是Oru模型漏洞的特征。作者在他的论文模型神经元开发的等式,具有平均射击率。在之前的论文中,我们展示了这个功能的柔化。到目前为止,这些方程已经成功维持,在恒定的设定点,在随机变化的流入条件下的罐中的水中的水平,并将卡车靠到装载码头。在本文中,我们展示了另一组等式的能力,该等式还采用了平均点火率概念,以便用连接到装载码头的拖车成功返回卡车。我们用于确定转向角(用于驾驶室或卡车的车轮)的算法比Kosko给出的众所周知(Kosko 1992)的众所周知。

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