Perceptron_Batch

MATLAB code for Random Sample Consensus (RANSAC)

说明：  本文件包含matlab辅助图像处理源程序(This document contains Matlab auxiliary source image processing)

圖檔大小自動縮放存出!!!!!!!!!!!!!!!!!!!!!!!!(Drawing size of the deposit automatically zoom out !!!!!!!!!!!!!!!!!!!!!!!!)

基于3G标准wcdma的上下行通信链路，基于3GPP协议，采用matlab simulink的仿真(Up and down based on 3G standards wcdma line communication link, based on 3GPP protocol, using matlab simulink simulation)

说明：  计算aic值，主要是用于ar模型算法中测算模型阶数(Aic value calculation, the main model algorithm is used to measure ar model order)

this program showing us the process of image enhancement

关于接连管道系统姿态解算的Matlab仿真，应用了四元数算法(Matlab simulation solver on a series of piping systems gesture, application of the quaternion algorithm)

自适应门限恒虚警检测的研究，主要是介绍恒虚警的一些基本理论及其仿真方法(Adaptive threshold CFAR detection introduced CFAR some basic theory and simulation method)

这是TD算法的逆过程，首先将时间序列，划分为相邻点的短序列，当然此时的拟合误差为0，因为第一点和第二点的连线，原始点都落在线段上。将相邻两个线段连接起来，此时每条线段包含三个原始点，计算中间那个点的拟合误差。这样，所有这些三个点的线段中的中间点的拟合误差计算出来后，找出误差最小且误差小于阀值R的分段，作为第一条包含三个点的线段。 在上面的基础上，第一条分段同样的和相邻线段连接，然后计算每一条分段的拟合误差，再找出误差最小且小于阀值R的分段，作为第二个分段。依次方式循环，直到所有分段的拟合误差都小于阀值R，分段结束。 (This is the reverse process of TD algorithm, the first time series is divided into short sequences adjacent dots, of course fitting error is zero at this time, because the first and second points of connection, the original point fall line on. The two adjacent segments are connected, then each segment contains three original point, the intermediate fitting error calculated that point. Thus, after all these segments fitting error of three points in the intermediate point calculated to find the minimum error and the error is less than the threshold R segment, as the first line contains three points. Based on the above, the first segment and the adjacent line segment connecting the same, and then calculate a fitting error for each segment, and then find the smallest error and less than the threshold R segment, as the second segment . Followed by a cyclic manner until all segments fitting error is less than the threshold R, segment ends.)

Linear prediction follows naturally from the general mathematics of linear systems. As the system output is defined as a linear combination of past samples, the system’s future output can be predicted if the scaling coefficients bj and ak are known.