FiberOpticsTechniciansManual2ndEdition[CuPpY]

机动目标跟踪的交互式多模型自适应滤波算法的程序设计代码 单传感器方位角和高低角(Maneuvering target tracking interactive multi-model adaptive filtering algorithm programming code)

说明：  ％ PURPOSE : Demonstrate the differences between the following filters on the same problem: ％ ％ 1) Extended Kalman Filter (EKF) ％ 2) Unscented Kalman Filter (UKF) ％ 3) Particle Filter (PF) ％ 4) PF with EKF proposal (PFEKF) ％ 5) PF with UKF proposal (PFUKF)( PURPOSE: Demonstrate the differences between the following filters on the same problem: 1) Extended Kalman Filter (EKF) 2) Unscented Kalman Filter (UKF) 3) Particle Filter (PF) 4) PF with EKF proposal ( PFEKF) 5) PF with UKF proposal (PFUKF))

基于WLAN室内无线环境，针对点到点之间的最短路径的采用floyd等算法实现仿真。(WLAN-based indoor wireless environment, the shortest path between point for the adoption of such floyd algorithm simulation.)

说明：  基于matlab的LDPC码的encoding和decoding的程序，强烈推荐初学者学习参考(Matlab based on the LDPC code encoding and decoding procedures, strongly recommended reference for beginners to learn)

用于生成黑白测试条纹图，可用于普通2D显示或者3D显示器的测试与开发。(Can be used for testing displays.)

模型辨识 模糊神经网络 T-S模型 输入输出 前提参数 结论参数(Model Identification of TS Fuzzy Neural Network model input and output parameters of the premise parameters Conclusion)

说明：   模拟退火算法求函数 f(x)的最小值点， 且 l <= x <= u f为待求函数，x0为初值点，l，u分别为搜索区间的上下限，kmax为最大迭代次数 q为退火因子，TolFun为函数容许误差 算法第一步根据输入变量数，将某些量设为缺省值( Simulated annealing algorithm of a function f (x) the minimum point, and l &lt = x &lt = u f is the unknown function, x0 as the initial point, l, u, respectively, lower and upper limits for the search interval, kmax is the maximum q iterations for the annealing factor, TolFun as a function of allowable error algorithm is the first step according to the number of input variables, set default values for certain amount of)

基于人工势场的岔路口的道路建模研究（程序）(Based on Artificial Potential Field Modeling road fork)

位同步仿真，有仿真数据的输入，以及转换，可以输入到verilog中仿真(Bit synchronous simulation, simulation data input, and conversion, can be entered into the simulation verilog)

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)