登录
首页 » DOS » whk

whk

于 2011-07-25 发布 文件大小:23KB
0 285
下载积分: 1 下载次数: 6

代码说明:

  平面六节点单元有限元分析,主要应用MATLAB的有限元分析(Planar six-node element finite element analysis, the main finite element analysis using MATLAB)

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 1
    说明:  fuzzy logic and we have fuzzy logic control to engginer
    2010-07-22 02:11:55下载
    积分:1
  • Multiscalemm
    数学形态学用于技术分析ECG心律的探测,很不错的(mathematical morphology for technical analysis of the ECG arrhythmia detection, very good)
    2007-03-05 15:51:12下载
    积分:1
  • geometricmoments
    利用matlab实现几何矩部不变量的简单算法。(The geometric moments of the invariant of the simple algorithm.)
    2012-05-22 08:11:05下载
    积分:1
  • randomsetslam
    Slam using random set theory
    2013-11-13 20:17:10下载
    积分:1
  • MatlabSimulationCaculation
    数字通信---基于matlab的仿真计算,PDF文本形式(Matlab Simulation Caculation)
    2012-01-16 20:36:48下载
    积分:1
  • endpointdetection
    end point detection is an matlab program for voice processing
    2010-11-06 22:26:54下载
    积分:1
  • extr
    Algorithms for the extraction of the extrema of a signal
    2013-04-08 20:33:45下载
    积分:1
  • MUSIC
    以前上高级信号处理的课程作业,是关于功率谱估计的MUSIC算法(Advanced signal processing on the previous course work, the power spectrum estimation on MUSIC algorithm)
    2011-11-28 17:48:03下载
    积分:1
  • Relevance-Vector-Machine
    说明:  相关向量机(Relevance Vector Machine,简称RVM)是Micnacl E.Tipping于2000年提出的一种与SVM(Support Vector Machine)类似的稀疏概率模型,是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的,在先验参数的结构下基于主动相关决策理论(automatic relevance determination,简称ARD)来移除不相关的点,从而获得稀疏化的模型。在样本数据的迭代学习过程中,大部分参数的后验分布趋于零,与预测值无关,那些非零参数对应的点被称作相关向量(Relevance Vectors),体现了数据中最核心的特征。同支持向量机相比,相关向量机最大的优点就是极大地减少了核函数的计算量,并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)
    2021-03-23 21:20:53下载
    积分:1
  • 231
    blurring and flat region filtering and noisy image
    2010-08-02 19:25:55下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载