mmse_siso

说明：  电力系统的牛顿-拉夫逊法潮流计算。 基本步骤： （1）形成节点导纳矩阵 （2）将各节点电压设初值U， （3）将节点初值代入相关求式，求出修正方程式的常数项向量 （4）将节点电压初值代入求式，求出雅可比矩阵元素 （5）求解修正方程，求修正向量 （6）求取节点电压的新值 （7）检查是否收敛，如不收敛，则以各节点电压的新值作为初值自第3步重新开始进行狭义次迭代，否则转入下一步 （8）计算支路功率分布，PV节点无功功率和平衡节点柱入功率。(Power system of Newton- rafer Johnson flow calculation method-matlab)

方程求根的源代码，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(Equation Root 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)

classification with showing the test and observation

说明：  一个casio dt 900 源代码，仅供参考！(A casio dt 900 source code for reference purposes only!)

ICA Clustering In computer science, Imperialist Competitive Algorithm (ICA)[1] is a computational method that is used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does not need the gradient of the function in its optimization process. From a specific point of view, ICA can be thought of as the social counterpart of genetic algorithms (GAs). ICA is the mathematical model and the computer simulation of human social evolution, while GAs are based on the biological evolution of species

Simulates DBPSK modulation.

Example Fuzzy C-Mean clustering kddcup99

simulink的入门教程，老师上课讲的课件，较易懂，适用于初学者(simulink introductory tutorial, the teacher talked about class courseware, more easy to understand for beginners)

this is new approach for fuel cell control.it is really helpfull.

虽然奇异值分解（SVD）和特征值分解（EVD的）是行之有效的，可以通过先进的设施设备先进的算法，它不是通常所说，有一个内在的迹象，可以显着影响的不确定性的结论计算及诠释来自其结果。我们提供一个解决方案，标志模糊的问题确定了从奇异向量的内积和个人数据载体签署奇异向量的迹象。该数据可能有不同的载体，但它有它自身的定位和实际意义的选择方向，其中多数的向量点。这可以通过评估发现了内心的签署标志产品的总和。(Although the Singular Value Decomposition (SVD) and eigenvalue decomposition (EVD) are well-established and can be computed via state-of-the-art algorithms, it is not commonly mentioned that there is an intrinsic sign indeterminacy that can significantly impact the conclusions and interpretations drawn from their results. We provide a solution to the sign ambiguity problem by determining the sign of the singular vector from the sign of the inner product of the singular vector and the individual data vectors. The data vectors may have different orientation but it makes intuitive as well as practical sense to choose the direction in which the majority of the vectors point. This can be found by assessing the sign of the sum of the signed inner products. )