matlab-tools-for-most-cases

计算给定时间序列的最大Lyapunov指数(compute the maximal Lyapunov exponent of a time series)

我编写bposs的神经网络应用程序，第一次上传(of neural network applications, the first upload)

LTE System Level Simulator 2.1

基于求解非线性薛定格方程的分步傅立叶方法和参变量积分法，在该论文中，我们提出了一种求解光脉冲在N芯光纤耦合器中传输信号的耦合模方程组的数值方法，同时用此方法对光脉冲在双芯和三芯非线性耦合器中的传输演化进行了仿真。(alculated using non-linear equation shrodinger received a variety of soliton solutions)

GPIF的详细介绍啊，里面有一些使用的例子。很不错的文档资料(A detailed description of GPIF ah, there are some use examples. Very good documentation)

有关再分析数据的MK检验MATLAB程序(test to analyze the data)

通过小波包分析计算柱塞流的特征值，奇异值(Through the wavelet packet analysis and calculation of the plunger flow characteristic value, singular value)

基于4G中802.16m系统的帧结构，通过运用发射分集技术提高其在瑞利信道中初始同步性能。(Based on 4G in the 802.16m system s frame structure, through enhances it using the launch diversity technology in the Rayleigh channel the initial synchronized performance. )

检验实际分布是否与理论密度分布函数相符合。球面均匀分布的随机点在二维平面上的投影的密度分布(Verify whether the actual distribution consistent with the theoretical density distribution function. Spherical uniformly distributed random dot density distribution projected on a two-dimensional plane)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)