kNNClassifier

使用matlab实现智能预测控制算法中的GPC算法，以阶跃、正弦等函数为例进行仿真实现 (Matlab intelligent use of predictive control algorithm of GPC algorithm to step, sinusoidal function, such as simulation as an example)

matlab 概率统计之经典算法教程matlab tutorial probability statistics of the classical algorithm(Probability and Statistics of the classical algorithm matlab tutorials matlab tutorial probability statistics of the classical algorithm)

Computing GLONASS accuracy (with statistic)

判别分析的码源，怎么说呢，就是好用，推荐下载！！(Discriminant analysis of the source code, how should I say, that is easy to use, recommended download! !)

小波包分析提取振动信号中的特征频率，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，进行逐步线性回归。( Wavelet packet analysis to extract vibration signal characteristic frequency, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, Stepwise linear regression.)

说明：  根据数据点的分布，对其进行曲线拟合，找到最合适的函数(According to the distribution of data points, curve fitting is carried out to find the most suitable function.)

KNN algorithm of matlab version.

参数估计，最大似染发、最小二乘法，Monte_Carlo法仿真10000点 (Parameter estimation, maximum likelihood hair, least square method, Monte_Carlo 10,000 points for Simulation)

This the simple books management system management system which Idevelops, hoped brings a help to the novice(This the simple books management system Idevelops management system which, hoped brings a help to the novice)

BP（Back Propagation）网络是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。 BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。 它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。 通过对信息的提取以及记忆，用作分类、聚类、预测等。 (BP (Back Propagation) network is a pre-press error back propagation algorithm Multilayer feedforward network, is one of the most widely used neural network model. BP network can learn and store a large amount of input- output mode mapping without prior reveal mathematical equations that describe the mapping relationship. It s learning rule is to use the steepest descent method, by reverse spread to constantly adjust the network weights and thresholds and the minimum sum of squared error of the network. BP neural network topology comprises an input layer (input), hidden layer (hide layer) and an output layer (output layer). By extracting information and memory, it is used as classification, clustering, prediction.)