lpr_loc

无刷直流电机的模糊PID控制 simulink仿真(Brushless DC motor simulink simulation of the fuzzy PID control)

说明：  用于小波滤波器的谱分析，mallat分解法。实现信号的重构(for wavelet spectral analysis, Mallat decomposition. Signal Reconstruction)

wavelet transform of multi-signal

灰色理论模型GM(1,1)预测客户需求 灰色理论模型GM(1,1)预测客户需求(Grey model GM (1, 1) forecast customer demand weight trends. Docx )

异步电动机转差频率控制系统仿真，在matlab7.0下运行没任何问题，转速、磁链等参数都有显示，适合想要学习电机控制方针和矢量控制的。G1=35，G2=0.15，G3=0.0076，G4=2，G5=G6=9.55(Asynchronous motor slip frequency control system simulation, under the matlab7.0 running without any problems,, according to the parameters such as speed, flux has suited to want to learn motor control principle and vector control.)

使用混沌与分形分析的例程，多姿态，多角度，有不同光照,多抽样率信号处理，包含优化类的几个简单示例程序，MIMO OFDM matlab仿真,本程序的性能已经达到较高水平。( Use Chaos and fractal analysis routines, Much posture, multi-angle, have different light, Multirate signal processing, Optimization class contains several simple sample programs, MIMO OFDM matlab simulation, The performance of the program has reached a high level.)

非常实用的基2DIT-FFT函数，直接运行可以分析原理。(Very practical base 2DIT-FFT function, operation can be analyzed directly principle.)

matlab中系统聚类法函数 用最短距离算法的系统聚类样本进行聚类(collectAnaly)

In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function f.(The secant iteration to find the zero of some non-linear function converges to zero if appropriate starting values are chosen. Although the convergence rate is not as good as the one of the Newton method, it may sometimes converge when Newton fails. The funtion should be defined as an inline function and two starting values are required to start the iteration. Please enter help secant from the Matlab prompt for usage information and examples. )

偏最小二乘法是一种数学优化技术，它通过最小化误差的平方和找到一组数据的最佳函数匹配。 用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(Partial least squares method is a kind of mathematical optimization technique, it through the minimum error sum of squares to find a set of data the best function matching. With the Jane s method for some absolutely unknowable true value, and make the sum of error square to a minimum.)