In this article we present a pattern classification system which uses Vector Symbolic Architecture (VSA) for representation, learning and subsequent classification of patterns, as a showcase we have used classification of vibration sensors measurements to vehicles types. On the quantitative side the proposed classifier requires only 1 kB of memory to classify an incoming signal against of several hundred of training samples. The classification operation into N types requires only 2∗N+1 arithmetic operations this makes the proposed classifier feasible for implementation on a low-end sensor nodes. The main contribution of this article is the proposed methodology for representing temporal patterns with distributed representation and VSA-based classifier.
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机译:在本文中,我们介绍了一种模式分类系统,该系统使用矢量符号架构(VSA)的图案,学习和随后的模式分类,作为我们已经使用振动传感器的分类对车辆类型的展示。在定量方面,所提出的分类器只需要1 kB内存来对来自几百个训练样本的传入信号进行分类。分类操作进入N类型仅需要2 * N + 1算术操作,这使得所提出的分类器可行用于低端传感器节点的实现。本文的主要贡献是所提出的方法,用于代表具有分布式表示和基于VSA的分类器的时间模式。
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