Facial-Recognition-System

Aplication for Matlab, for a problem using SVM

说明：  这是粒子群优化算法的实现，求解多模态函数非常使用(This is the Particle Swarm Optimization algorithm to solve multi-modal function is the use of)

Seismic analysis just for fun

提出了一个混沌系统,并对该系统的基本动力学特性进行了深入研究.得到该系统的Lyapunov指数、Lyapunov 维数,给出了Poincaré映射图以及时域图和相图.运用电子工作平台EWB软件对实现该新混沌系统的振荡器电路进行了仿真实验.经过数值仿真和电路系统仿真证实该系统与以往发现的混沌吸引子并不拓扑等价,属于新的混沌系统.(Presents a chaotic system, and the basic dynamic characteristics of the system were studied. Get Lyapunov exponent of the system, Lyapunov dimension, given the Poincar maps and graphs and time-domain phase diagram using electronic work platform EWB software for implementing the new chaotic system oscillator circuit simulation experiments through numerical simulation and circuit and system simulation confirmed that the system ever discovered chaotic attractor is not topologically equivalent, is the new chaotic system.)

给定栅格图像及其变形后的栅格图像，请你首先确定各栅格交叉点的位移，然后采用控制栅格的方法编写实现图像几何校正的程序(Given its raster image raster image after deformation, you first determine the displacement of each grid intersection, and then using the method of preparation of the control grid of image geometric correction procedures)

关于LDPC的。是Mackcy网站上的LDPC编译码程序。(On the LDPC. Mackcy site is LDPC coding and decoding procedures.)

基于matlab简单计算器，和我们平时用的普通计算器一样(Matlab prepared easy to use calculator)

用直接解得办法求摄影测量中的相对定向元素( use direct measurement to find the relative orientation elements in photography)

说明：  洛伦兹混沌多涡卷吸引子源代码，复杂程序，用于毕业设计(Multi-scroll chaotic Lorenz attractor source code, complex procedures for graduation)

排课问题是一个有约束的、多目标的组合优化问题，并且已经被证明是一个NP完全问题。 遗传算法借鉴生物界自然选择和自然遗传机制，使用群体搜索技术，尤其是用于处理传统搜索方法难以解决的复杂的和非线性的问题。经过近40年的发展，遗传算法在理论研究和实际应用中取得了巨大的成功，本文将遗传算法用于排课问题的求解，首先讨论了排课问题中的影响因素、主要约束条件、求解目标和难点，并用数学模型完整地描述了排课问题。其次对多个模糊排课目标进行了定量分析，建立了排课优化目标空间。针对排课问题研究了染色体编码方式以及遗传算子的设计，提出了适应度函数的计算方法。最后对排课问题进行了实验。实验结果表明，其过程的目标值跟踪显示，算法稳健趋优，所得结果令人满意。(Course Scheduling problem is a constrained, multi-objective optimization problem, and has proven to be a NP complete problem. Genetic algorithms reference biosphere and the natural genetic mechanism of natural selection, using the group search technology, particularly the traditional search methods for handling complex and difficult to solve nonlinear problems. After nearly 40 years of development, the genetic algorithm in the theoretical study and practical application was a great success, this paper genetic algorithm for solving the course timetabling problem, first discussed the impact of factors in the course arrangement, the main constraints, to solve goals and difficulties, and a complete mathematical model to describe the course arrangement. Arranging multiple fuzzy goals followed by a quantitative analysis, the optimal target Arranging space. Arranging for the Study of the chromosome coding and genetic operators design, proposed fitness function is calculated. Finally, the co)