the-method-to-remove-noises

主要根据一个简单的电网潮流计算实例，来说明潮流计算的具体步骤，以及计算过程中出现的一些问题(Mainly based on a simple power flow calculation examples to illustrate the specific steps flow calculation, and calculation of some of the problems arising in the course)

matlab 算法 (matlab algorithm)

优化设计方法 --------无约束优化方法(design optimization methods-------- unconstrained optimization methods)

模糊PID 调试成功 先运行fuzzypid.m,在运行fuzzypiduse.m(Fuzzy PID successful commissioning of power function and simulink simulation)

遗传算法求旅行商，送货商，邮递员，灾情巡视等问题，是一种近似算法。(Genetic algorithm to solve the traveling salesman, delivery companies, mail carriers, disaster inspections and other issues, is an approximation algorithm.)

使用matlab simulink 编写的钟摆运动(use matlab simulink to do a clock move)

DTFT plot the spectrum of a sinusoidal signal

说明：   在三次样条中，要寻找三次多项式，以逼近每对数据点间的曲线。在样条术语中，这些数据点称之为断点。因为，两点只能决定一条直线，而在两点间的曲线可用无限多的三次多项式近似。因此，为使结果具有唯一性。在三次样条中，增加了三次多项式的约束条件。通过限定每个三次多项式的一阶和二阶导数，使其在断点处相等，就可以较好地确定所有内部三次多项式。此外，近似多项式通过这些断点的斜率和曲率是连续的。然而，第一个和最后一个三次多项式在第一个和最后一个断点以外，没有伴随多项式。因此必须通过其它方法确定其余的约束。最常用的方法，也是函数spline所采用的方法，就是采用非扭结(not-a-knot)条件。这个条件强迫第一个和第二个三次多项式的三阶导数相等。对最后一个和倒数第二个三次多项式也做同样地处理。 (the Cubic Spline, to find three polynomial, approaching every right to point the data curve. The kind of terms, these data points called breakpoints. Because only two points a straight decisions, and in between the two curves can be used an infinite number of cubic polynomial approximation. Therefore, in order for the outcome of uniqueness. The Cubic Spline, add the cubic polynomial bound by the conditions. By limiting each cubic polynomial of the first order and second-order derivative, it breakpoint in the same Department, we can better identify all internal cubic polynomial. In addition, the polynomial approximation of these breakpoints slope and curvature is continuous. However, the last one and a cubic polynomial in a breakpoint and the final one, not accompanied by polynomials. There)

Integrity toolbox. For glucose investigation purposes

说明：  支持向量机用于分类问题的演示程序，分线性与非线性两部分(support vector machines for the demonstration classification procedures, sub-linear and nonlinear part 2)