20100528KFDA

this is the another matlab code for gui signals

最经典的人工神经网络教程，内容详细,适用于广大战友(Classic artificial neural network tutorial, detailed and apply to the majority of his comrades)

这个是关于车牌识别方面的源代码，是一种基于灰度定位的。(This is on the license plate recognition source code, is based on the gray level positioning.)

文档，瑞利衰落信道的matlab源程序，可以单独运行。 -word documents, Rayleigh fading channels Matlab source that can run.(Rayleigh fading channels Matlab source that can run.)

ica application algorithm to classify data in an supervised way

Single input single output matlab simulation code.

一种实现复矩阵的SVD分解的算法，使用Matlab进行仿真，亲测可用。(SVD decomposition algorithm to achieve complex matrix, using Matlab to simulate pro-test is available.)

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)

集合了多种频谱分析方法，利用matlab进行编程分析，文件含有多种代码。(Collection of a variety of spectral analysis method , using matlab programming analysis , the file contains a variety of code )

用MATLAB实现并行以及快行的DDC,验证通过(MATLAB DDC)