SingleSystemMPC

说明：  轮廓波工具箱，是一个非常有用的图像分解和合成变换(contourlet toolbox)

处理降雨量日志数据的小程序，去掉异常值，换算，统计，作图(Applet rainfall log data, remove outliers, conversion, statistics, mapping)

粒子群神经网络的预测编程实现,电力负荷预测本人已实现(Particle Swarm neural network predictive programming, I realized the power load forecast)

This program describes the use of Inverse Synthetic Aperture Radar (ISAR) with stepped-frequency waveform (SFWF) to produce a high-resolution, 2D image of a target.

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)

说明：  用来进行双边带幅度调制DSB_AM的MATLAB源程序。(Demonstration script for DSB-AM. The message signal is+1 for 0 < t < t0/3,-2 for t0/3 < t < 2t0/3, and zero otherwise.actually means Rx(m-1). )

检查matlab运行情况的程序,matlab下直接运行.(a good software to check the performance of matlab)

加权网络的源代码，对于研究复杂网络模型很有用，可以参考哈^_^(BBV model-weighted network strength and weight distribution are the model of power.)

STC波形处理方法，提取波列慢度，应用于地震资料处理与测井资料处理中(STC waveform processing method to extract wave train slow degrees, applied to seismic data processing and logging data processing)

Após o pré-processamento (prep), os dados sã o reduzidos pelo algoritmo SVD e calcula-se os Scores (T) e os Loadings (P). Posteriormente, plota-se T e P, na forma de gráficos biplots