eemizer_v1.0

matlab中生成各种噪声的程序，包括brown噪声,pink噪声，白噪声等(matlab noise generated in a variety of procedures, including the brown noise, pink noise, white noise, etc.)

小波分析及其应用的matlab程序，都是示列，对于学习利用matlab进行小波分析很有用(Matlab wavelet analysis and its application procedure are shown out, for learning using wavelet analysis is useful matlab)

灰色神经网络的预测算法—订单需求预测matlab源代码(Gray neural network prediction algorithm- order demand forecast)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

Iterative learning neural network control for nonlinear system trajectory tracking

说明：  读取地震数据软件，是地震资料处理的基础性资料(seismic software)

本程序可将输入的RGB图像转化为灰度图像！在Matlab环境下编程实现。(this procedure can be imported RGB images into gray image! Matlab programming environment.)

一个9输入4输出的矩阵，利用BP神经网络训练并预测。通过不断的调试获取最可靠地输出。(A 9-input 4-output matrix, using BP neural network training and prediction. Continue to debug access to the most reliable output.)

本代码主要实现了基于双字典交叉稀疏表示的SAR图像变化检测的功能。(This code mainly realize the function of SAR image change detection is based on the double cross dictionary sparse representation.)

array weighting models fort smart antennas