space工具箱

armax_sinus armax_sinus armax_sinus armax_sinus armax_sinus

在降低运算量的同时保持了相控阵接收通道回波之间的固有幅度、相 位关系, 采用变换法来产生通道噪声, 使通道回波生成耗时控制在毫秒量级及系统仿真实现准 实时。(In reducing the amount of computing while maintaining a phased array receiver channel between the intrinsic echo amplitude, phase relations, the use of transform methods to generate channel noise, echo generation takes control of the channel in the order of milliseconds and the system simulation to achieve accurate real-time.)

Elementary graphics editing.Sources MFC application that was created in VIsual C ++. Run the program and draw the strip.

游程编码是一种简单的非破坏性资料压缩法，其好处是加压缩和解压缩都非常快。(RLE is a simple non-destructive data compression method, the added advantage of very fast compression and decompression. The method is to calculate the length of consecutive compression of the data.)

极化SAR矩阵的相互转换的程序，包含s、k、c、t矩阵的转换(The conversion of Pol-SAR matrixes，including s，k,c,t matrixes)

可应用于最小二乘的计算，对数据进行回归，定量分析等(It can be applied to the computation of least squares, regression of data, quantitative analysis, etc.)

This package contains functions that implement Principal Component Analysis (PCA) and its lesser known cousin, Independent Component Analysis (ICA). PCA and ICA are implemented as functions in this package, and multiple examples are included to demonstrate their use. In PCA, multi-dimensional data is projected onto the singular vectors corresponding to a few of its largest singular values. Such an operation effectively decomposes the input single into orthogonal components in the directions of largest variance in the data. As a result, PCA is often used in dimensionality reduction applications, where performing PCA yields a low-dimensional representation of data that can be reversed to closely reconstruct the original data. In ICA, multi-dimensional data is decomposed into components that are maximally independent in the negentropy sense. ICA differs from PCA in that the low-dimensional signals do not necessarily correspond to the directions

完整的逆序动态规划程序，含详细注释，最优策略的完整输出(Complete reverse dynamic planning process, with detailed notes)

program to construct a siusoidal signal

syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )(syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' ))