Kmeans

rbfnn用于长期预测，效果比多元回归模型好。(rbfnn for long-term forecast, the effect is better than the multiple regression model.)

在雷达和声纳的信号设计中，经常用的模糊函数，本程序给出了传统信号的模糊函数及仿真结果，适用于MATLAB初学者。(Sonar and radar signal design, often using the fuzzy function, this procedure gives the traditional signal ambiguity function and simulation results, suitable for beginners MATLAB. )

说明：  对物种灭绝的一个马尔科夫链仿真，利用循环和概率来进行逐步仿真。(For a Markov chain simulation of species extinction, cycle and probability are used for step-by-step simulation.)

说明：  研究小波阈值去噪算法和经验模态分解算法，对心磁信号的去噪过程(Study the denoising process of wavelet threshold denoising algorithm and empirical mode decomposition algorithm for magnetic heart signal)

主要是仿真了gold码、数据的调制、加噪、解调以及数据恢复的过程。(Simulation of gold is mainly a code, data modulation, noise, demodulation and data recovery process.)

this code extract the feature from speech signal in many algorithms

随着新的数学工具小波分析的实用化为基于NN负荷预测模型性能的改善提供了理论依据对于电力系统负荷非线性时间序列的辨识在预测方法研究中应给予重视在本文所用的基于小波原理和NN融合的预测原理是具有强的非线性时间序列的辩能力由研究和仿真表明它能有效提高预测的精度(with new mathematical tools wavelet analysis based on NN into practical load forecasting model to improve the performance of the rationale On the basis for the power system load nonlinear time series forecasting methods in literacy research should give importance to the use of paper Based on wavelet theory and NN fusion principle is the forecast of strong nonlinear time series by the dialectical capacity of research and simulation shows it can effectively improve the accuracy of forecasts)

Classification of ictal and seizure-free EEG signals using fractionallinear prediction

数字信号处理实践方法（第2版）书中有关的代码，很有实用价值(Digital signal processing practices (2nd Edition) book-related code, and very practical value)

support vector classification machine ％ soft margin ％ uses "kernel.m" ％ ％ xtrain: (Ltrain,N) with Ltrain: number of points N: dimension ％ ytrain: (Ltrain,1) containing class labels (-1 or +1) ％ xrun: (Lrun,N) with Lrun: number of points N: dimension ％ atrain: alpha coefficients (from svcm_train on xtrain and ytrain) ％ btrain: offest coefficient (from svcm_train on xtrain and ytrain) ％ ％ ypred: predicted y (Lrun,1) containing class labels (-1 or +1) ％ margin: (signed) separation from the separating hyperplane (Lrun,1(support vector machine classification% s oft margin% uses "kernel.m"%% xtrain : (Ltrain, N) with Ltrain : number of points N : % ytrain dimension : (Ltrain. 1) containing class labels (-1 or 1)% xrun : (Lrun, N) with Lrun : number of points N : % atrain dimension : alpha coefficients (from svcm_train on xtrain and ytrain)% btrain : offest coefficient (from svcm_train on xtrain and ytrain)%% ypred : predicted y (Lrun. 1) containing class labels (-1 or 1)% margin : (signed) separation from the separating hyper plane (Lrun, 1)