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案例5

于 2018-03-01 发布文件大小:58KB

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基于BP_Adaboost的强分类器设计——公司财务预警建模(Design of strong classifier based on BP_Adaboost: modeling of corporate financial early-warning)

文件列表：

案例5\Bp_Ada_Fore.m, 1833 , 2010-01-30
案例5\Bp_Ada_Sort.m, 2289 , 2010-01-30
案例5\data.mat, 11820 , 2009-12-28
案例5\data1.mat, 46394 , 2009-09-09
案例5, 0 , 2010-01-30

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maze solution. it is a solution for maze problem with matlab code. in this solution I used very simple functions and it is easy for everyone.

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MUSIC算法[1] 是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(algorithm MUSIC is a method based on matrix feature space decomposition. From a geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, it is clear that these two spaces are orthogonal. Signal subspace by the array received data covariance matrix and signal corresponding to the feature vector and noise subspace is by the covariance matrix of all minimum feature values (noise variance) corresponding to the feature vectors are composed. MUSIC algorithm is to use the orthogonal characteristics of these two complementary space to estimate the azimuth of spatial signals. All vectors of the noise subspace are used to construct spectral, and the peak position in all spatial azimuth spectra correspond to the direction of arrival of the signal. MUSIC algorithm greatly improves the direction finding resolution, and at the same time it is suitable for the antenna array of any shape, but the prototype MUSIC algorithm requir)

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快速对偶增广拉格朗日算法，目标函数的最优化求解，常用于逆问题的求解，是典型的L1范数算法。(Quick optimization for dual augmented Lagrangian algorithm for solving the objective function, commonly used in inverse problem solving, is typical of L1 norm algorithm.)

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matlab code to check convergence of error.

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案例5

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