mpiv_toolbox

this helps for understanding insar simulation

归一化的函数，神经网络的必须。希望对大家有用。。。。(Normalized function, neural networks have. Hope to be useful. . . .)

德克萨斯大学奥斯汀分校Baldick Ross教授关于不等式约束最优化问题的课件。详细阐述了最优潮流等典型最优化问题。(Baldick Ross Professor of the University of Texas at Austin courseware inequality constrained optimization problem. The typical elaborate OPF most optimization problems.)

为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识 别。对同一负荷下的已知故障信号进行变分模态分解，利用 奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法 应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value decomposition was proposed for fault diagnosis using standard fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were decomposed by VMD, and the modes characteristics were further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the test samples were clustered by a Hamming nearness approach, and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under variable loads. By comparing with a method based on EMD, this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)

ber for bpsk modulation in ofdm channel

说明：  动力系统中的分支程序，主要研究动力系统的周期(Branch of dynamical systems program, the main research cycle power system)

lms adaptive algorithms

多种小波去噪的方法，包括阈值，一维和二维(Multiple wavelet denoising methods, including threshold, one and two dimensional)

利用matlab自带的BP神经网络工具箱对duffing混沌背景模型进行相空间重构与预测，程序中已经给出duffing函数，主函数中也给出了主要注释，方便大家学习交流。(BP' s own use matlab neural network toolbox for duffing chaotic background model and prediction of phase space reconstruction, the program has been given duffing function, the main function also gives the main note, we facilitate learning exchanges.)

说明：  CSF布料滤波算法PYTHON+MATLAB(CSF PYTHON+MATLAB code)