NeighborhoodCLASSIFIER

最小二乘辨识系统模型参数的算法，可以用于辨识离散模型的参数(Least squares algorithm for model parameter identification system can be used to identify the parameters of the discrete model)

离散论域模糊控制表的离线计算.已知单变量两维输入，一维输出模糊控制器，其两维输入为E，EC，一维输出为I，论域均为E，EC，I∈{-6，-5，-4，-3，-2，-1，0，1，2，3，4，5，6}，E的论域划分为{NB，NM，NS，NZ，PZ，PS，PM，PB}，共8个模糊子集(Discrete domain of fuzzy control table offline computing. Known univariate two-dimensional input, one-dimensional output fuzzy controller, the two-dimensional input of E, EC, one-dimensional output as I, on the field are E, EC, I ∈ {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, E of the domain is divided into {NB, NM, NS, NZ, PZ , PS, PM, PB}, a total of eight fuzzy subset)

人体三维数据精简方法，运用平面切割的方法，即先平面切割，再留值(Human dimensional data reduction methods, the use of cutting plane methods, that is, first cutting plane, then leave the value)

介绍了matlab的各种学科工具箱，如信号处理工具箱，神经网络工具箱等(Introduced a variety of disciplines matlab toolbox, such as the Signal Processing Toolbox, Neural network toolbox, etc.)

matlab programmer plc

阵列信号处理常用的MUSIC算法，包括经典MUSIC，求根MUSIC，基于四阶累积量的MUSIC等(MUSIC usual array signal processing algorithms, including the classic MUSIC, Root MUSIC, based on fourth-order cumulant MUSIC etc.)

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。(Genetic Algorithm (Genetic Algorithm) is a simulation of natural selection and genetics of Darwinian biological evolution computational models of biological evolution, the process is a way to search the optimal solution by simulating the natural evolution.)

对融合图片质量的评价指标，包括峰值信噪比和均方根误差,非常好用(the ution index of image fusion.include PSNR and RMSE ,it is very useful)

自适应滤波器LMS算法的误差曲线和误差平方曲线，并在等高线上绘出LMS100次迭代的平均轨迹曲线，配合数字信号处理2课程的学习(LMS adaptive filter algorithm and error squared error curve curve and contour line drawing LMS100 iterations the average trajectory curve, with 2 digital signal processing courses)

说明：  这是我从网上摘抄下来的，关于matlab语法与常用函数要点，供大家一起学习。(This is the excerpt from the Internet down, on the matlab function of grammar and common elements for all to learn together.)