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The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa. For example, >> project.name = MyProject >> project.id = 1234 >> project.param.a = 3.1415 >> project.param.b = 42 becomes with str=xml_format(project, off ) "<project> <name>MyProject</name> <id>1234</id> <param> <a>3.1415</a> <b>42</b> </param> </project>" On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).

非线性方程求解的的源代码，matlab编写的，值得收藏，欢迎大家踊跃下载，为中国的科研事业做贡献(Non-linear equation of the source code, matlab prepared and worthy of collection, enthusiastically welcomed the U.S. download, in order to make China' s contribution to the cause of scientific research)

一种matlab匹配算法，计算图像的7个不变矩 (A matlab matching algorithm to calculate the images of seven invariant moments)

自适应波束形成，对波束形成的初学者很有用。运行过绝对正确(Adaptive beamforming, useful beginner beamforming. Ran absolutely correct)

调频连续波仿真程序。完成调频连续波的调制仿真。(Frequency Modulated Continuous Wave Simulation)

Binary cat search optimization algorithm

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

基本PSO算法，可以根据迭代资料生成不同的图像，很形象。绝对原创。(Basic PSO algorithm based on iterative information to generate a different image, the very image. Absolute originality.)

基于matlab的峰值检测算法,具有较为直观地意义，系数可以根据实际需要进行调节(Matlab-based peak detection algorithm, with a more intuitive sense, the coefficients can be adjusted according to actual needs)

该界面可以用来分析控制系统在时域，频域和使用状态空间分析技术。它还提供了在采用极点配置的方法设计一个二阶系统的状态空间设计额外的特征。(This GUI can be used to analze control systems in time domain, in frequency domain and using using state space analysis techniques. It also provides an additonal feature of designing a second order system in state space design using pole placement approach. )