A Target Value Control While Training the Perceptrons in Changing Environments

S. Raudys
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引用次数: 3

Abstract

To ensure fast adaptation and security of social and computerized systems to changing environments, targets of perceptron based classifiers ought to vary during training process. To determine optimal differences between target values (stimulation, arousal) we suggest using genetically evolving multi-agent systems aimed to extract necessary information from sequences of the changes. A specially designed additional feedback chain allows updating the target values faster.
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变化环境下感知器训练的目标值控制
为了保证社会和计算机系统对不断变化的环境的快速适应和安全,基于感知器的分类器的目标应该在训练过程中变化。为了确定目标值(刺激,唤醒)之间的最佳差异,我们建议使用旨在从变化序列中提取必要信息的遗传进化多智能体系统。特别设计的附加反馈链允许更快地更新目标值。
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