BAM Learning of Nonlinearly Separable Tasks by Using an Asymmetrical Output Function and Reinforcement Learning

著录项

• 来源
  《Neural Networks, IEEE Transactions on》 |2009年第8期|p.1281-1292|共12页
• 作者

  Chartier S.; Boukadoum M.; Amiri M.;

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• 作者单位

  Sch. of Psychol., Univ. of Ottawa, Ottawa, ON, Canada;

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• 收录信息
• 原文格式 PDF
• 正文语种 eng
• 中图分类
• 关键词

  content-addressable storage; learning (artificial intelligence); neural nets; regression analysis; BAM learning; asymmetrical output function; bidirectional associative memory networks; chaotic wandering; cognitive modeling viewpoint; dual fixed-point behavior; general regression neural network; nonlinearly separable tasks; reinforcement learning; Bidirectional associative memory (BAM); chaos control; cusp catastrophe; hybrid model; prior knowledge;

  机译：内容可寻址存储;学习（人工智能）;神经网络;回归分析;BAM学习;不对称输出函数;双向联想记忆网络;混沌徘徊;认知建模观点;双重定点行为;通用回归神经网络;非线性可分离任务强化学习双向联想记忆（BAM）混沌控制尖峰突变混合模型先验知识;