hybridPSO

用matlab编写的关于设计滤波因子的源代码(Prepared with matlab filtering factor for the design of the source code)

研究光子晶体的系统有参考价值的源程序，对科研有帮助。(Photon crystal, important and useful for research.)

一种叫做优劣排序结果与顺序结构评估法的决策方法，可用于多目标选择最终解(a decision making method called Preference Ranking Organization Method for Enrichment Evaluation, PROMETHEE, can be used for selecting the final solution of multi-objective optimization)

QAM调制的ＭＡＴＬＡＢ程序仿真，思路清晰，既可以学习如何用ＭＡＴＬＡＢ编程，又可以理解通信调制技术！(QAM MATLAB simulation procedures, clear thinking, can learn how to use MATLAB programming, but also to understand communication modulation technology!)

matlab有关数学建模的资料，在数学建模中有很大的帮助(Matlab relevant mathematical modeling of the material, in mathematical modeling a lot of help )

本matlab函数，利用混沌映射不可预测性，遍历性和敏感性，以它们的参数和初始值的优异特性的，实现一个图像加密算法。我们引入了一个新的二维logistics函数。与现有的混沌映射相比，它具有更广泛的范围内混乱，更好的遍历性，混沌性和relativelylow实施成本。为了研究及其应用，我们提出了一个之乱转换（CMT），以有效地改变图像的像素位置。结合2D-logistics与CMT，我们进一步实现图像加密算法。(In this matlab test,we use the chastic s excellent properties of unpredictability, ergodicity and sensitivity to their parameters and initial values, chaotic maps are widely used in security applications. In this paper, we introduce a new two-dimensional Sine Logistic modulation map (2D-SLMM) which is derived the Logistic and Sine maps. Compared with existing chaotic maps,it has the wider chaotic range, better ergodicity, hyperchaotic property and relativelylow implementation cost. To investigate its applications, we propose a chaotic magic trans-form (CMT) to efficiently change the image pixel positions. Combining 2D-SLMM with CMT, we further introduce a new image encryption algorithm. )

传染病经典模型si，sis，sir编程以及其图像和实现(Classic model of infectious diseases si, sis, Sir Programming and its image and implementation)

自适应干扰对消噪声，有用信号为语音，干扰为高斯白噪声，变步长的NLMS算法性能分析(Adaptive interference cancellation noise and useful signals for voice, interference is Gaussian white noise, variable step size NLMS algorithm performance analysis)

说明：  梯度下降算法代码及详细解释，使用matlab编程(Gradient descent algorithm code and detailed explanation)

Gradient information serves several purposes. It can relate the structure of objects in an image, identify features of interest for recognition/classification directly or provide the basis of further processing for various computer vision tasks. For example, "edges," noted by regions of high gradient magnitude, are central to the task of identifying defects in circuitry. A sample "edge-detected" image using the Image Processing Toolbox for MATLAB is shown where locations marked by white are those points that are indicative of high gradient magnitude, which can also be described as regions of high pixel contrast.