SISO

利用matlab编制的利用单纯形处理工程问题的程序(compiled by the use of Matlab using simplex works, the procedure)

ThreeLevelDWT. it explain different level and ycrcb

Matlab code for Variable Selection

说明：  使用光线跟踪算法生成室内可见光通信脉冲响应的MATLAB代码(Using ray tracing algorithm to generate matlab code of indoor visible light communication pulse response)

适合初学者学习的matlab手册，并且有常用的例程(Suitable for beginners to learn matlab manuals, and a commonly used routine)

Characteristics of the closed one-dimensional object 2

This file is GUI in MATLAB that use for load flow in the network.

当计算混沌系统Lyapunov指数时，gs.m代码实现正交化 直接运行L2.m，可计算出Lyapunov指数（该代码是基于奇异值分解计算Lyapunov指数的） (chaotic system when calculating Lyapunov exponent, gs.m code directly Orthogonalization L2.m operation, translate into Lyapunov exponent (The code is based on the singular value decomposition Lyapunov exponent))

matlab智能算法程序例子，例子中详细介绍了神经网络法遗传算法粒子群算法等解决寻优问题(Examples of intelligent algorithm matlab program, described in detail in the example neural network genetic algorithm particle swarm algorithm to solve the optimization problem)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域(Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications)