DYNIC-OF-MATLAB

采用改进的K最近邻算法对混沌时间序列进行预测(The improved K-nearest neighbor algorithm to predict chaotic time series)

稀疏分解图像重建程序，把图像分解成多个小块图像，然后再各个子块重建后边缘处理后合并成整个图像。(sparse decomposition image reconstruction process, the image is divided into a number of small images, then each sub-block redevelopment edge after the merger into the whole image.)

说明：  The program can detect the phase difference between one Sinoid signal with another sinoid signal and can generate a new signal of same phase

这是一个综合性的仿真程序。包括：用matlab生成一个FIR滤波器，得到滤波器系数及阶数；再用C语言产生一个混合频率的数字信号，之后将生成的数据样点送入到DSP主程序进行数字滤波，DSP主程序算法用的是循环缓冲区算法，这一部分在CCS上实现。(This is a comprehensive simulation program. Comprising: generating a matlab FIR filter, to obtain the filter coefficients and order then mixed to produce a C-frequency digital signal, and then the resulting data samples into the main program to the DSP for digital filtering, DSP main algorithm using a circular buffer algorithm, this part of the CCS to achieve.)

calssification algorithm66

function [x,n] = stepseq(n0,n1,n2)

a logistic regression in matlab

matlab结合神经网络的工具包使用说明，简便易懂，好东西(combined with neural network matlab toolkit for use, easy to understand, good stuff)

说明：  用matlab实现匀速模型下的卡尔曼滤波器功能的源代码(Constant velocity model Kalman filter simulation)

城市交通流的运行存在着高度的复杂性、时变性和随机性,实时准确的交通流预测是智能交通系统,特别是先进的交通管理系统与先进的出行者信息系统研究的关键. 基于交通流预测的特点,给出了基于遗传算法的小波神经 网络的交通预测模型GA-WNN ,用具有自然进化规律的遗传算法来对小波神经网络的连接权值和伸缩平移尺度进行前期优化训练,部分代替了小波框架神经网络中按单一梯度方向进行参数优化的梯度下降法,克服了单一梯度下降法易陷入局部极小和引起振荡效应等缺陷. 仿真实验验证了GA-WNN 预测模型对短时交通流的预测的有效性.(For the high complexity ,time-variation and probability of urban traffic flow , its real-time and exact prediction is critical to the research of intelligent traffic systems , especially for the advanced traffic manage-ment system and advanced traveler information system. Based on the character of the traffic flow prediction , a GA-WNN model is given based on the wavelet neural network with genetic algorithm. The genetic algorithm of natural evolving law for the gradient descendent algorithm in Wavelet Neural Network is partly substituted to pre-optimize the connection weight and the extension scale of the wavelet neural network and later optimize the parameters along a single gradient vector. This method overcomes some drawbacks when there exists a single gradient descendent algorithm , such as local minimum and oscillation. A short-time traffic flow prediction sim- ulation using the GA2WNN prediction model demonstrates the validity of the model .)