MaxLLoyd

------名称：带交叉因子的改进PSO算法 ------功能：求解多维无约束优化问题 ------特点：收敛性强，还可以加入变异算子(------ Name: with cross-factor function to improve the PSO algorithm ------: Solving multi-dimensional unconstrained optimization problem ------ characteristics: strong convergence, mutation operator can also add son)

and this for chapter 4, chapter 3 just describe some theories.

GPS的RMC、CGATT等格式帧的数据定位解析(GPS locate)

位场向下延拓的泰勒级数方法，利用拉普拉斯方程的特点，计算空间域水平二阶差分来替代垂直方向上的二阶差分(Potential field downward continuation of Taylor series method, the advantage of the characteristics of Laplace equation, a second order differential spatial domain levels instead of second order difference vertically)

本代码设计的切比雪夫1型滤波器，与MATLAB中的切比雪夫滤波器不同的是输出为未归一化的零极点，输入只要是通带频率，通带波动，截止频率，阻带衰减即可，更便于抄做(The code design of Chebyshev Type 1 filter, and MATLAB in the Chebyshev filter output is not different is that the poles and zeros of the normalized input as long as the pass-band frequency, pass-band fluctuations, cutoff frequency, stopband attenuation can be more easily copied to do)

用PId控制算法，实现永磁直线电机的速度与位置的跟踪(With PId control algorithm to achieve the permanent magnet linear motor speed and position tracking)

利用MCMC来取柯西分布的随机数，使用的是M-H采样。(MCMC to take advantage of the Cauchy distribution random number, using MH sampling.)

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)

小波包的分层和提取特征，提供给大家参考，在研究小波中。(Layered and extract wavelet packet provided for your reference in the study wavelet.)

可以实现粗糙集的整个算法，从上下近似、属性约简、属性重要度到决策规则的输出和规则的确定性因子，全部实现。内附测试数据，有详细的注解，可以实现与数据库的直接连接。(Rough set can be achieved throughout the algorithm, from top to bottom approximation, attribute reduction, attribute importance to the uncertainty factor output of decision rules and regulations, all to achieve. Enclosing the test data, detailed notes, can be achieved with a direct connection to the database.)