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一种非线性PID控制算法在卡车ABS中的应用及仿真研究，提出NPID控制器的建模()

多目标粒子群算法优化分布式发电选址 实现了最优目标(Mutil-Objective particle swarm optimization)

一维 二维 三维分形布朗运动的序列生成，以及相空间生成(One-dimensional 2D and 3D fractal Brownian motion sequence generation and generation phase space)

Google KML Contour Plot / Wind Arrows Plot

zzzzz_qftdemos matlab/simulink

无模型控制的matlab代码，采用m文件形式，直接可运行，参数预估采用带遗传因子的最小二乘法实现(Model free control matlab code m file can be run directly, the parameters estimated using the least squares method with genetic factors)

优化 Bitmap的小程序 希望对大家有用我人有很有借鉴意义(Optimize the Bitmap small program hope useful for everyone I have a lot of reference)

ZFFT的matlab实现，可以输入任意序列以及欲观察的频段进行观察。使用时请将解压后的文件放置到matlab目录下，通过调用函数ZFFT实现。(ZFFT matlab realize, you can enter any sequence and want to observe the band was observed.)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。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。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)

lucas kanade optical flow detection-segmentation-