Heat-Conductoin-2-D

说明：  NSGA2多目标优化算法，配上说明文档 NSGA-Ⅱ算法是 Srinivas 和 Deb 于 2000 年在 NSGA 的基础上提出的，它比 NSGA算法更加优越：它采用了快速非支配排序算法，计算复杂度比 NSGA 大大的降低；采用了拥挤度和拥挤度比较算子，代替了需要指定的共享半径 shareQ，并在快速排序后的同级比较中作为胜出标准，使准 Pareto 域中的个体能扩展到整个 Pareto 域，并均匀分布，保持了种群的多样性；引入了精英策略，扩大了采样空间，防止最佳个体的丢失，提高了算法的运算速度和鲁棒性。(NSGA2 multi-objective optimization algorithm, with instructions)

宏观经济增长所需的代码，包括经济增长与经济波动，详实全面，好用好(The code needed for macroeconomic growth, including economic growth and economic fluctuations, detailing a comprehensive, easy to use good)

用adaboost算法生成基支持向量机分类器，并对识别结果进行简单投票法集成。附有支持向量机工具箱和adaboost算法流程说明。(Adaboost algorithm to generate the base with a support vector machine classifier, and the recognition result is a simple voting method integration. With support vector machine algorithm toolbox and adaboost process description.)

国外优秀地震信号分析处理软件的细部功能全英文介绍，提供相关算法的思路及参考文献(seismic signal analysis and processing software in detail functions are all the English introduction, provide ideas and references of the algorithm)

基于卡尔曼的视频序列帧图像的跟踪程序，还有condense算子法的跟踪，可动态读取序列帧并对其进行估计跟踪，非常好的程序。(based video sequence pictures of the tracking process, in addition to condense operator law tracking, Dynamic sequences can be read on its frame and estimated tracking, very good procedures.)

it contain all the necessary documents for beamformer and antennas you find bartllet,capon,music,esprit.

哈夫变换提取参数 matlab实现 附带测试图像(Hough transform extracted parameters to achieve incidental test images matlab)

耦合影响格子序列仿真，随机性，平衡性，级多瓣分治混沌序列在cnma中的应用(Simulation of the impact of lattice coupling sequence, randomness, balance, class of chaotic sequence多瓣divided the application in cnma)

This is a matlab version of Fisher linear discriminant classifier

径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)