Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

A. T. Burrell; P. Papantoni-Kazakos

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Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

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摘要

We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.

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《通讯、网络与系统学国际期刊（英文）》 |2012年第9期|603-608|共6页
作者
A. T. Burrell; P. Papantoni-Kazakos;
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作者单位

Computer Science Department;

Oklahoma State University;

Stillwater;

USA;

Electrical Engineering Department;

University of Colorado Denver;

Denver;

USA;

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原文格式 PDF
正文语种 chi
中图分类数学;
关键词
Qualitative; Robustness; Predictive; Model; Mapping; Stochastic; Approximation; Stochastic; Binary; Neural; Networks; Real-Time; Supervised; Learning; Ergodicity;

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Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

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