期刊文献+

混沌光子储备池计算研究进展 被引量:8

Research Progress of Reservoir Computing Using Chaotic Laser
原文传递
导出
摘要 混沌光子储备池计算是一种利用光混沌系统作为储备池实现信息处理的新技术,具有处理速度快、计算容量大、物理实现简单等优点,在未来光子计算机、智能信息处理等领域有广阔的应用前景。详细介绍了混沌光子储备池计算的概念、原理、实现过程和实现方案,比较了各种实现方案的优缺点。总结了目前混沌光子储备池计算中一些有待解决的问题,展望了混沌光子储备池计算的未来发展趋势。 Chaotic photonic reservoir computing is a new information processing technique, which employs chaotic laser as the reservoir. The advantages of the new technique are of high processing speed, big computing capacity and simple physical implementation. This new kind of computing can be applied in the future photonic computer, intelligent information processing and other fields. This paper introduces the concept, principles, processes and implementation of chaotic photonic reservoir computing in detail. The advantages and disadvantages of different implementation schemes are compared. Some issues to be resolved for the chaotic photonic reservoir computing are listed. The trends of chaotic photonic reservoir computing are also demonstrated.
出处 《激光与光电子学进展》 CSCD 北大核心 2013年第3期23-29,共7页 Laser & Optoelectronics Progress
基金 国家自然科学基金(61071123 61172059) 区域光纤通信网与新型光通信系统国家重点实验室(北京大学)开放基金 教育部博士研究生学术新人奖资助课题
关键词 光计算 光信息处理 储备池计算 神经网络 混沌激光 optics in computing optical information processing reservoir computing neural network chaotic laser
  • 相关文献

参考文献28

  • 1R.S.Tucker.The role of optics in computing[J].Nature Photon.,2010,4(7): 405.
  • 2H.J.Caulfield,S.Dolev.Why future supercomputing requires optics[J].Nat.Photonics,2010,4(5): 261-263.
  • 3D.Woods,T.J.Naughton.Optical computing: photonic neural networks[J].Nature Phys.,2012,8(4): 257-259.
  • 4M.Lukoeviius,H.Jaeger.Reservoir computing approaches to recurrent neural network training[J].Computer Science Review,2009,3(3): 127-149.
  • 5M.C.Ozturk,D.Xu,J.C.Principe.Analysis and design of echo state networks[J].Neural Computation,2007,19(1): 111-138.
  • 6罗熊,黎江,孙增圻.回声状态网络的研究进展[J].北京科技大学学报,2012,34(2):217-222. 被引量:27
  • 7W.Maass,T.Natschlager,H.Markram.Real-time computing without stable states: a new framework for neural computation based on perturbations[J].Neural Computation,2002,14(11): 2531-2560.
  • 8D.Verstraeten,B.Schrauwen,M.D′Haene et al..An experimental unification of reservoir computing methods[J].Neural Networks,2007,20(3): 391-403.
  • 9M.Lukoeviius,H.Jaeger,B.Schrauwen.Reservoir computing trends[J].KI-Künstliche Intelligenz,2012,26(4): 365-371.
  • 10彭宇,王建民,彭喜元.储备池计算概述[J].电子学报,2011,39(10):2387-2396. 被引量:19

二级参考文献158

共引文献69

同被引文献34

引证文献8

二级引证文献30

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部