fisher

svm 支持向量机分类程序，模式识别作业(svm for patter recognition assignment)

汽车改进型ABS仿真程序，用于分析装备ABS后汽车的动力学性能，需要与carsim进行联合仿真。(The car improved the ABS simulation program used to analyze the dynamics of the car after the equipment ABS performance, co-simulation with carsim.)

It is a matlab code to make a masking in spatial domain for an image. it contains different type of masking procedure for image processing.

simulating center location(simulation)

Bee algorithmBee algorithmBee algorithm(1)信号处理、(2)人工智能与模式识别、(3)神经网络、(4)图像处理、(5)数据挖掘、(Bee algorithm3)

lingjian.m-----蒙特卡罗方法 lingjian.m使用零件初始值，用蒙特卡罗方法算出总费用。其中使用了自己编制的正态分布随机数发生器产生正态分布随机数。lingjian.m是对蒙特卡罗方法的一次练习。 accyouhua为标定值的函数，而lingjian不是一个函数，在其中已给出了一组标定值的值。 退火确定标定值/unitanneal()----模拟退火 连续型多个变量组合优化问题 这是对模拟退火方法的一次练习，结果证明模拟退火确实是一个行之有效的方法。 当参数选择较好时（一般也伴随着运行时间的加长），模拟退火的结果较好，然而用MATLAB的FMIMCON()一般可达到更高的精度。(lingjian.m---- Monte Carlo method. 氀椀渀最樀椀愀渀.m Part initial value, using the Monte Carlo method to calculate the total cost. The preparation of their own normal distribution random number generator to generate normally distributed random numbers. lingjian.m is the first practice of the Monte Carlo method. accyouhua calibration value the function lingjian not a function, which gives the value of a set of calibration values. Annealing to determine the calibrated value/unitanneal ()---- Simulated Annealing 吀栀攀 continuous multiple variables combinatorial optimization problems 吀栀椀猀 is an exercise of the simulated annealing method, the results show that the simulated annealing is an effective method. 圀栀攀渀 the parameter selection is better (generally accompanied by a longer running time), simulated annealing results, however using MATLAB FMIMCON () generally achieve higher accuracy.)

关于最优化理论中DFP算法的matlab实现的简单方法。(On optimization theory DFP algorithm matlab realize the easy way.)

slam simulation for robot cartoggraphy and mapping with out external sensors

meshgrid method in matlab

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S (1) still lacks enough robustness to noise and outliers and (2) is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm).