Multi-Sensor-Data-Fusion-with-MATLAB

层次分析中确定权重的方法，主要是针对聚类中没有出现交叉情况下确定权重(The level of analysis to determine the weight of the method, is mainly directed against cross-clustering does not appear to determine the weight of circumstances)

基于滑动时间窗的支持向量机软测量建模研究，是一片不错的论文，对做软测量的研究生学子们可以参考(Sliding time window based on support vector machine soft sensor modeling is a good paper, to do a soft measuring graduate students who can refer)

分析了该信号的时域、频域、倒谱，循环谱等,时间序列数据分析中的梅林变换工具，可以得到很精确的幅值、频率、相位估计，LCMV优化设计阵列处理信号，matlab小波分析程序，外文资料里面的源代码。( Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Time series data analysis Mellin transform tool, You can get a very accurate amplitude, frequency, phase estimation, LCMV optimization design array signal processing, matlab wavelet analysis program, Foreign materials inside the source code.)

改进的背景差分法，结合了背景差分法和帧间差分法的优点，能更好的进行视频处理。(Improved background subtraction method, combining the advantages of the background difference and frame difference method, and better video processing.)

SVPWM的matlab程序实现，源程序逐句有注释。(SVPWM matlab program implementation, the source was previously published annotated.)

计算尺寸因子对消光效率和散射效率，吸收效率，后向散射效率等的影响(calculation of the size factor extinction efficiency and the efficiency of scattering, absorption efficiency, after scattering efficiency of)

可计算序列的信息熵、两个序列间的联合信息熵和互信息(This file can calculate the entropy, joint entropy and mutual information)

Mutual Information computation

this is code for making the excel table in matlab for procesing your data.

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)