fusion

说明：  matlab超经典的使用教程，内附详细图解，让你快速掌握常用算法(matlab Tutorial ultra classic, containing detailed illustrations, so you quickly master the commonly used algorithm)

toolbox to calculet integral progection model

The C++ Programming Language.Third Edition - Bjarne Stroustrup.1997.pdf(The C++ Programming Language.Third Edition- Bjarne Stroustrup.1997.pdf)

Crown.Dreaming in Code.2007

matlab仿真程序：扩频发射接收机用匹配滤波器的方法同步(matlab simulation program: spread-spectrum receiver with matched filter fired way synchronization)

遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法，它最初由美国Michigan大学J.Holland教授于1975年首先提出来的，并出版了颇有影响的专著《Adaptation in Natural and Artificial Systems》，GA这个名称才逐渐为人所知，J.Holland教授所提出的GA通常为简单遗传算法（SGA）。(GA (Genetic Algorithm) is a simulation of Darwinian theory of evolution by natural selection and genetic mechanism of biological evolution of the computing model is a natural evolutionary process by simulating the optimal solution search method, which originally developed by the University of Michigan J.Holland Professor in 1975 first proposed and published an influential monograph " Adaptation in Natural and Artificial Systems" , GA This name was gradually known, J.Holland raised by Professor GA is usually simple genetic algorithm (SGA ).)

用于演化博弈中，列出复制动态方程后，求解复制动态方程的均衡点(Solving the equilibrium point of the replication dynamic equation)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

一个关于网页设计的简单有效的matlab编码(A simple and effective web design matlab coding)

Exact Recovery of Sparsely-Used Dictionaries