BLUR-AND-DEBLUR

说明：  学习联合检测的圣经啊，经典算法的经典解析(the bible of styding JD, classical introduction of classical alglorithms)

基于MMSE算法的自适应均衡LMS算法仿真，采用AR模型(MMSE algorithm based on LMS adaptive equalization algorithm simulation, the use of AR model)

运用四元数方法进行捷联惯性系统的姿态解算，并由加速度计的数据和陀螺的数据计算导航参数(The four element method for strapdown inertial navigation system of the attitude calculation, and the accelerometer and gyroscope data data computation of navigation parameters )

我们可以对任意图像利用游程编码进行编码和解压。(We can use any image coding and run-length coding decompression.)

电力系统暂态稳定程序，可以进行暂态稳定计算,是学习PCA特征提取的很好的学习资料，pwm整流器的建模仿真。( Power System Transient Stability Program, can be transient stability, Is a good learning materials to learn PCA feature extraction, Modeling and simulation pwm rectifie.)

matlab数值分析与应用线性方程组数值方法(linear simultaneous equations )

使用matlab软件搭建出来的光伏电池放电模型，波形很理想(Using matlab software to build out solar battery discharge model is ideal waveform)

利用R的toepliz性质，是实数，则r也应该是实数，是实信号r(-m)=r(m)(Toepliz the nature of the use of R is real number, then r should be real, real signal r (-m) = r (m))

matlab与word、Excel的混合使用，太强了，该程序包括各种实例，狂赞！(Matlab and the word, Excel mixed use, too. The procedures include various examples of mad praise!)

Rosenbrock函数优化属于无约束函数优化问题，其全局极小值位于一条平滑而狭长的抛物线形状的山谷底部，且为优化算法提供的信息很少，因此找到其全局极小值就显得很困难。根据Rosenbrock函数的这种特性，专门提出了一种改进的PSO算法，该算法引入三角函数因子，利用三角函数具有的周期振荡性，使每个粒子获得较强的振荡性，扩大每个粒子的搜索空间，引导粒子向全局极小值附近靠近，避免算法过早地收敛，陷入局部最优，从而找到Rosenbrock函数的全局极小值。大量实验结果表明，该算法具有很好的优化性能，为某些领域某些特定的类似于Rosenbrock函数的优化问题提供了一种新的思路。(Rosenbrock function optimization functions are unconstrained optimization problems, its global minimum value at a smooth parabolic shape and narrow mountain valley Department, and for the optimization algorithm provides little information, and therefore find its global minimum value becomes very difficult. According to this feature Rosenbrock function, specifically mentioning Out an improved PSO algorithm (PSO-R), the algorithm introduced trigonometric factor, using trigonometric has periodic oscillations, so that each particle to obtain more Strong oscillations of expanding the search space of each particle, the particle is close to nearby guide global minimum, avoiding premature convergence algorithm, into a local optimum, thus Finding the global minimum Rosenbrock function. Experimental results show that the algorithm has a good optimizing performance, similar to some certain areas Rosenbrock function in the optimization problem provides a new way of thinking.)