期刊
ORGANIC ELECTRONICS
卷 25, 期 -, 页码 16-20出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.orgel.2015.06.015
关键词
Memristor; Perceptron; Pattern classification; Machine learning; Polyaniline; Neuromorphic computing
资金
- Russian Foundation for Basic Research [15-29-01324]
- MaDEleNA project - Provincia Autonoma di Trento
Elementary perceptron is an artificial neural network with a single layer of adaptive links and one output neuron that can solve simple linearly separable tasks such as invariant pattern recognition, linear approximation, prediction and others. We report on the hardware realization of the elementary perceptron with the use of polyaniline-based memristive devices as the analog link weights. An error correction algorithm was used to get the perceptron to learn the implementation of the NAND and NOR logic functions as examples of linearly separable tasks. The physical realization of an elementary perceptron demonstrates the ability to form the hardware-based neuromorphic networks with the use of organic memristive devices. The results provide a great promise toward new approaches for very compact, low-volatile and high-performance neurochips that could be made for a huge number of intellectual products and applications. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据