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Internet-based remote control by using Adaline neural networks

机译:使用Adaline神经网络的基于Internet的远程控制

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In this paper, we present a remote control scheme for Internet-based teleoperation. This control scheme relies on the real-time estimation of concurrent roundtrip delays in order to optimally assign tasks between the user and the robot. For this purpose, we employ an adaptive linear (Adaline) neural network for which most conventional learning algorithms are infeasible since the computation is usually too intensive to be practical. To get around this problem, we introduce a novel learning algorithm that is based on the maximum entropy principle. Compared to traditional learning algorithms, the computing cost of this algorithm is very low, which makes it possible for the proposed neural network to be implemented on-line in real-time.
机译:在本文中,我们提出了一种基于Internet的远程操作的远程控制方案。该控制方案依赖于并发往返延迟的实时估计,以便在用户和机器人之间最佳地分配任务。为此,我们采用了一种自适应线性(Adaline)神经网络,对于该网络而言,大多数传统的学习算法都不可行,因为计算量通常过于密集而无法实用。为了解决这个问题,我们引入了一种基于最大熵原理的新颖学习算法。与传统的学习算法相比,该算法的计算成本非常低,这使得所提出的神经网络可以实时在线实现。

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