一个spea2多目标进化算法，MATLAB编译

matlab新增寻找极值点程序，较老的版本中没有次函数(matlab find new extreme point of procedure, not the older version of the second function)

鸡群优化算法模仿了具有等级秩序的鸡群寻找食物的过程，将鸡群分成几个小组，小组成员包括公鸡、母鸡和小鸡，其中公鸡为领导者，母鸡随机选择小组，母鸡与小鸡间的母子关系随机建立。三种身份根据适应度值来决定，较优的适应度值所对应的鸡作为公鸡，较差的适应度值所对应的鸡作为小鸡，其余的作为母鸡。鸡群成员在跟着公鸡去寻找食物的过程中，等级秩序、母子关系不会改变。

生成（0,1）随机序列，白噪声。生成M序列。(Generating (0,1) random sequence, the white noise. Generate an M-sequence.)

一个好玩的Matlab游戏，启发你学会编辑代码，趣味练习的帮手(A fun Matlab game, inspired you to learn to edit code, fun exercises helper)

curvelet transform and ridgelet transform source code

说明：  微波光子滤波器能够实现在光域内直接对射频信号的处理,并且较易实现可调谐性和传输函数的快速重构,从而成为微波光子学的一个重要研究内容.本代码提供了一般性的结构仿真，并且还说明了切趾响应对MSSR的影响。(Microwave photon filter can process RF signal directly in optical domain, and it is easy to realize tunability and fast reconstruction of transmission function, which has become an important research content of microwave photonics. This code provides general structure simulation, and also explains the effect of toe cutting on MSSR.)

说明：  一个A*算法的应用案例，在存在多种障碍物的环境下，实现智能体的航路规划(An application case of a * algorithm is used to realize the route planning of the agent in the presence of multiple obstacles)

说明：  边缘检测的新方法，与传统边缘检测方法的比较(new edge detection method with the traditional edge detection method of comparison)

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)

这段代码是用来求希尔伯特变换的，希望对大家有用(The Hilbert transform )