Adaptive learning in random linear nanoscale networks
Adaptive learning in random linear nanoscale networks
复制标题
随机线性纳米级网络中的自适应学习
DOI：
10.1109/nano.2011.6144633
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Hsing
中科院分区：
文献类型：
--
作者：
M. Anghel;C. Teuscher;Hsing
虽然自上而下的工程CMOS技术有利于规则和局部互连的结构，但新兴的分子和纳米级自下而上自组装器件将由大量简单、密集排列的元件构建而成，这些元件表现出高故障率、相对较慢和以无序方式连接。这样的系统不能通过标准方式进行编程。在这里，我们提供了一个有监督学习问题的解决方案，即在具有给定控制节点的线性函数的随机纳米级网络中，将期望的二进制输入映射到期望的二进制输出。这种网络模型的灵感来源于自组装的银纳米线。我们的结果表明，单控制节点和双控制节点随机网络可以实现线性可分集。
While the top-down engineered CMOS technology favors regular and locally interconnected structures, emerging molecular and nanoscale bottom-up self-assembled devices will be built from vast numbers of simple, densely arranged components that exhibit high failure rates, are relatively slow, and connected in a disordered way. Such systems are not programmable by standard means. Here we provide a solution to the supervised learning problem of mapping a desired binary input to a desired binary output in an random nanoscale network of linear functions with given control nodes. The network model is inspired after self-assembled silver nanowires. Our results show that one- and two-control node random networks can implement linearly separable sets.