suijixinhaotongjizhi

基于支持向量机分类器的活动形状模型。学术论文。(Support vector machine classifier based on the activities of the shape model. Papers.)

eJERCICIO DE iCARBEGIE 7

Some useful wireless communication lectures 3.

预测函数控制的英文书籍很有用，包含很多内容(English books useful predictive function control contains a lot of content)

基于鱼群算法的椭圆检测，对于一组随机的椭圆点，可以得到椭圆参数，检测精度高。(Ellipse detection algorithm based on fish, for a group of random ellipse point, the parameters can be oval, high detection accuracy.)

《智能控制第三版仿真程序》刘金琨著所有源程序。("Intelligent control third version of the simulation program" Liu Jinkun with all the source program.)

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)

The purpose of this program is to calculate the refractive index of a certain material. And you should import some basic optical parameters firstly.

自适应最优核时频分析程序及相关软件，可用于相关的时频分析(Adaptive Optimal Kernel Time-frequency analysis procedures and related software for the relevant time-frequency analysis)

Li near r egr essi on, acti ve learning We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion where the go al is to pr e dict a con tin uous resp onse y t ∈ R to e ach example ve ctor. Here to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e easily extended (ne xt lecture).