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Effects of Global Perturbations on Learning Capability in a CMOS Analogue Implementation of Synchronous Boltzmann Machine

机译：同步Boltzmann机CMOS模拟实现中全局摄动对学习能力的影响

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摘要

All of the presented implementations of Artificial Neural Networks (A.N.N.) have been supposed to be working in ideal conditions, however, real applications will be subject to local and global perturbations. Since 1994, we have investigated the behaviour modeling of electronic A.N.N. with global perturabtion conditions. We have scrutinised the behaviour analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability of the above mentioned CMOS based analogue implementation. Simulation and experimental results have been exposed validating our concepts.
机译：人工神经网络（A.N.N.）的所有提出的实现都应该在理想条件下工作，但是，实际应用将受到本地和全局干扰。自1994年以来，我们研究了电子A.N.N.的行为建模。具有全球摄动条件。我们已经仔细研究了同步玻尔兹曼机模型在环境温度和电扰动下的CMOS模拟实现的行为分析。在本文中，我们使用我们的模型介绍了这些全局扰动对上述基于CMOS的模拟实现的学习能力的影响的分析。仿真和实验结果已经曝光，验证了我们的概念。

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