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Control engineering aguid for beginners

本例用五点差分法求解一个二阶偏微分方程，其间用Jacobi迭代求解(This example uses a five-point difference method for solving second order partial differential equations, during which the Jacobi iterative)

一个基于信息最大化生成显著图的程序对于彩色或红外图像都有很好的效果(Based information maximization generate significant chart program has a good color or infrared image effect)

本程序仿真了高斯噪声的几种概率密度估计方法(This procedure simulated several Gaussian noise probability density estimation method)

Plot antenna arrays in 3D

一个计算混沌时间序列相关维数的matlab程序(A calculation of chaotic time series correlation dimension of the matlab program)

粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来的一种新的进化算法(（Evolu2tionary Algorithm - EA）。PSO 算法属于进化算法的一种，和遗传算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。(Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years ((Evolu2tionary Algorithm- EA) .PSO algorithm is an evolutionary algorithm, and similar genetic algorithm, it is also starting random solutions, through iterative search for the optimal solution, which is also uated by the quality of the fitness solution, but it is much simpler than genetic algorithms rule, it does not have the genetic algorithm cross (Crossover) and variant (Mutation) operation, which by following the current search to find the optimal value to the global optimum. the algorithm for its easy implementation, high accuracy, and fast convergence advantages attracted academic attention, and in solving practical problems demonstrated its superiority.)

Implementation of Digital predistortion in simulink using Saleh Memoryless Non-linearity and Least Squares method. I modified inbuilt OFDM block with Raised cosine block

spea2多目标进化算法对两个目标的多目标优化问题的测试(spea2 multi-objective evolutionary algorithm on the two goals of testing multi-objective optimization problem)

An example that illustrates the importance of shift-invariance is image denoising by thresholding where the lack of shift-invariance causes pseudo-Gibbs phenomena around singularities.In addition to shift-invariance, it has been recognized that an efficient image representation has to account for the geometrical structure pervasive in natural scenes. In this direction, several representation schemes have recently been proposed