Cavalcade neural network for mobile robot

C. Bamford, R. Mitchell
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引用次数: 1

Abstract

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a robot.
移动机器人的Cavalcade神经网络
由于其复杂的数学模型和缺乏直观的学习算法,脉冲神经网络的应用通常受到限制。本文提出了一种更简单、新颖的神经网络,这种神经网络是由漏积分和火神经元模型衍生而来的,即“cavalcade”神经元。开发了神经网络的仿真,并在环境中实现了两种基本的学习算法。这些算法成功地学习了一些基本的时间和瞬时问题。然后从这些实验中获得神经网络结构的灵感,并将其应用于处理传感器信息,从而成功地控制机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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