shuangjixingguilingma

MATLAB工具箱集锦(非官方资料) 希望对于Matlab编程爱好者有所帮助！(MATLAB toolbox items (non-official information) hope for the Matlab programming lovers some help help!)

雨流算法的matlab程序，我运行过了，可以使用-(The rainflow algorithm matlab program, I run, you can use the-)

CDMA系统中的多用户检测技术性能比较，包括MMSE,DC，OMD(CDMA system in multi-user detection performance comparison, including the MMSE, DC, OMD)

基于峭度的盲源分离，使用最速下降法，适合初学者(BSS based on kurtosis by gradient)

主要用于控制Boost电路的电压和电流，使用了神经网络，FGM，ADMM等控制算法(Mainly used to control the voltage and current Boost circuit using a neural network, FGM, ADMM and other control algorithm)

文章讨论了一种用于分析复杂的多态特征空间的非参数技术——Meanshift方法，并利用它进行了任意的形状聚类。(A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it.)

这是可控金字塔（steerable pyramids）的代码，使用Matlab 编写(This is controllable pyramid (steerable pyramids) code, the use of Matlab to prepare)

利用MATLAB的SIMULINK仿真的串级控制系统仿真源码(The SIMULINK simulation using MATLAB cascade control system simulation source)

现代信号处理中常用的处理信号的滤波器，卡尔曼和维纳滤波器(Modern signal processing that are commonly used in signal processing, Kalman filters and Wiener filter )

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 .)