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首页> 外文会议>International work-conference on artificial and natural neural networks >Effects of Global Perturbations on Learning Capability in a CMOS Analogue Implementation of Synchronous Boltzmann Machine
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Effects of Global Perturbations on Learning Capability in a CMOS Analogue Implementation of Synchronous Boltzmann Machine

机译：全球扰动对同步Boltzmann机器CMOS模拟实现中学习能力的影响

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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：所有呈现的人工神经网络（A.N.N.）的实现已经在理想的条件下工作，但是，实际应用将受到当地和全球扰动的影响。自1994年以来，我们研究了电子A.N.N的行为建模。具有全球erturabtion条件。我们对环境温度和电气扰动进行了仔细审查了同步Boltzmann机模型的CMOS模拟实现的行为分析。在本文中，我们使用我们的模型，分析了这些全局扰动对基于CMOS的模拟实现的学习能力的影响。仿真和实验结果已暴露验证我们的概念。

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