多元线性回归计算程序

应用背景matlab仿真硬件时用的定点化仿真,一般用matlab 直接生成的滤波器响应仿真，因为结果是浮点的，所以和真实硬件计算出的结果差别很大。所以应当进行定点化处理，再检查响应曲线，在硬件设计（FPGA）就应该提前清楚到底要多和位的精度。关键技术输入参数是归一化的浮点数以及位宽，bit_floor.m用来仿真低位直接截断后的效果。bit_round.m是硬件按真正的四舍五入截断处理后的效果。bit_round_hw.m是仿真硬件处理里简化版四舍五入。

这个文件是用来模拟QDPSK的误码率的。结果被写入一个txt文件，然后您可以将其导入matlab。

GAUSS-SEIDEL法的Matlab程序-A programm realizing GUASS-SEIDEL algorithm,written in Matlab.

详细表述了分数阶傅立叶变换的离散实现，将现有的算法进行比较。-In this note we make a critical comparison of some matlab programs for the digital computation of the fractional Fourier transform that are freely available and we describe our own implementation that lters the best out of the existing ones. Two types of transforms are considered: First the fast approximate fractional Fourier transform algorithm for which two algorithms are available. The method is described in H.M. Ozaktas, M.A. Kutay, and G. Bozdagi. Digital computation of the fractional Fourier transform. IEEE Trans. Signal Process., 44:2141{2150, 1996. There are two implementations: one is written by A.M. Kutay the other is part of package written by J. O Neill. Secondly the discrete fractional Fourier transform algorithm described in the master thesis C . Candan. The discrete fractional Fourier transform, Bilkent Univ., 1998 and an algorithm described by S.C. Pei, M.H. Yeh, and C.C Tseng: Digital fractional Fourier transform base

The source includes a recursive template version and a non-recursive template version

该音频处理算法涉及：压缩器、扩展器、限幅器三种音频处理的常用算法，使用C++编写。

这是一个关于BP神经网络的程序，对初学者有一定的帮助。-This is a study on BP neural network procedures, these will certainly help beginners.

应用背景迷宫问题，有需要的可以下，就是简单的迷宫代码，如果有需要的可以拿去看一下，仅供参考关键技术 简单的c++代码，关于迷宫问题，有人需要的可以拿去下载，仅供参考，谢谢！！！！

用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为S(t)（1） 统计特性的初值为 （2）观测误差是与和均不相关的白噪声序列，并且有 （3）观测数据存放在附加的文件中（单位：m）。 要求：分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求： （1） 距离S(t)的最佳估计及估计误差， （2） 距离S(t-5)的最佳平滑及估计误差， （3） 距离S(t+5)的最佳预测及估计误差， （4） 对结果进行分析讨论。 -By one observer from the t = 1 PST on a moving target tracking for distance measurement, assuming that the observation interval is one second, the radar that the distance between the moving target for the S (t) (1) the statistical characteristics of the initial condition (2) observational error is not associated with white noise sequence, and (3) observational data stored in the attached document (unit: m). Requirements: Analysis of the above-mentioned object, the establishment of the system model, constructed Kalman filter, programming terms, seeking: (1) distance from S (t) the best estimate and the estimation error, (2) distance from S (t-5) the most good smoothing and estimation error, (3) distance from S (t+ 5) t

数十个程序，结构分析，偏重岩土方面-dozens of procedures, structural analysis, emphasizing geotechnical aspects