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this file includes a matched filter implementation in matlab in a radar system

基于matlab的小波神经网络算法的水文时间序列预测，基于1步和基于五步预测的程序，由于原始数据偏少，预测精度稍微差点(Hydrologic time series matlab wavelet neural network algorithm based on prediction, based on a step and five-step program to predict, because the raw data too few, almost a little prediction accuracy )

these m files contain the solution of some problems in the book "communication system" by Haykin

模式识别的一部分，用于确定二值边界的，不错的算法额(Part of a pattern recognition algorithm for binary boundary determined )

说明：  计算直流潮流的算法，基于MATLAB，忽略无功功率，只考虑有功功率。(DC Power Flow Algorithms)

An object oriented Circular buffer implementation. Matlab version 7+ is required,

分数阶系统的计算工具，解决一般解及动力学行为的问题(tools for solving fractional-order systems)

matlab实现基于时域有限差分法模拟电磁传播的源代码(matlab source code to achieve based on the Finite Difference Time Domain Simulation of Electromagnetic Propagation)

fast kmeans for matlab

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)