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MDSP
清华现代数字信号处理参考书籍、课程重点及复习大纲,清华电子工程系张颢(Tsinghua modern digital signal processing reference books, course outlines priorities and review, Tsinghua Electronic Engineering Zhang Hao)
- 2021-03-20 10:59:18下载
- 积分:1
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Python核心编程(第二版)
python核心编程书籍,适合小白入门的一本书强烈推荐(Python core programming books)
- 2020-06-17 08:20:01下载
- 积分:1
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JanErikSolem.ProgrammingComputerVision
Programming Computer Vision with Python
by Jan Erik Solem
- 2019-02-06 15:40:06下载
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《小白学SAS》书中配套程序
《小白学SAS》配套材料适读人群 :SAS用户,统计、数据分析领域的相关工作人员(sas course, Readable population: SAS users, relevant staff in the field of statistics and data analysis)
- 2020-06-18 04:00:01下载
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winAVR
winAVR中文教程winAVR中文教程winAVR中文教程(failed to translate)
- 2009-07-04 09:21:28下载
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KPCA_RVM
关于关联向量机应用的最新文献!
提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)
- 2020-06-29 05:40:01下载
- 积分:1
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Hp-Computing-Parallel-Computing
高性能并行计算
迟学斌 中国科学院计算机网络信息中心(High-performance parallel computing Chi Xuebin, Chinese Academy of Sciences Computer Network Information Center)
- 2013-03-13 11:07:20下载
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3
说明: MATLAB语言在建筑抗震工程中的应用3(Application of MATLAB Language in Building Seismic Engineering 3)
- 2020-06-23 20:20:02下载
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MIMO
学习MIMO的入门课件!能够很快的了解MIMO的原理!写的很详细!(MIMO-entry learning courseware! Be able to quickly understand the principle of MIMO! Written in great detail!)
- 2009-09-21 20:29:46下载
- 积分:1
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深入理解C#(中文第三版)
深入理解C#(中文第三版)pdf文档 带目录清晰版(C# in depth)
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