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BP神经网络在电池分选中的应用 被引量:3
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作者 武云丽 李革臣 陈松林 《哈尔滨理工大学学报》 CAS 2001年第5期10-13,共4页
针对电池分选中,由于输入技术参数存在噪声而引起误选率过高的问题,以 BP(badk-propagation)神经网络为基础,使用抽样学习的方法,提出了多参数输入的电池分选方案.实验结果表明,该方法能在技术参数存在噪声... 针对电池分选中,由于输入技术参数存在噪声而引起误选率过高的问题,以 BP(badk-propagation)神经网络为基础,使用抽样学习的方法,提出了多参数输入的电池分选方案.实验结果表明,该方法能在技术参数存在噪声的情况下,通过网络训练可减小分选误差. 展开更多
关键词 电池分选 BP神经网络 抽样学习 误选率 分选误差
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How Supportive a Good Learner's Dictionary Can Be in Language Learning:A Sample Study on Macmillan English Dictionary for Advanced Learners 被引量:12
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作者 Ding Jun 《Fudan Journal of the Humanities and Social Sciences》 2009年第3期119-128,共10页
This paper illustrates the language-learning-supporting features of a monolingual English learner's dictionary, the Macmillan English Dictionary for Advanced Learners ( 2002 ) , a product of " a learner-based phil... This paper illustrates the language-learning-supporting features of a monolingual English learner's dictionary, the Macmillan English Dictionary for Advanced Learners ( 2002 ) , a product of " a learner-based philosophy. " It gives a detailed mapping of the user-friendliness of the dictionary in question and points out that MEDAL is helpful to learners of English mainly because it answers both their decoding and encoding needs. To further explore the potential of a good learner's dictionary, the current author also discusses the role of an English teacher in helping his/her students to make better use of the dictionary, and consequently ensures the supportiveness of MEDAL in the language learning process. 展开更多
关键词 MEDAL ENCODING DECODING English learning
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The convergence rates of Shannon sampling learning algorithms 被引量:2
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作者 SHENG BaoHuai 《Science China Mathematics》 SCIE 2012年第6期1243-1256,共14页
In the present paper,we provide an error bound for the learning rates of the regularized Shannon sampling learning scheme when the hypothesis space is a reproducing kernel Hilbert space(RKHS) derived by a Mercer kerne... In the present paper,we provide an error bound for the learning rates of the regularized Shannon sampling learning scheme when the hypothesis space is a reproducing kernel Hilbert space(RKHS) derived by a Mercer kernel and a determined net.We show that if the sample is taken according to the determined set,then,the sample error can be bounded by the Mercer matrix with respect to the samples and the determined net.The regularization error may be bounded by the approximation order of the reproducing kernel Hilbert space interpolation operator.The paper is an investigation on a remark provided by Smale and Zhou. 展开更多
关键词 function reconstruction reproducing kernel Hilbert spaces Shannon sampling learning algorithm learning theory sample error regularization error
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