dynamic-unloading

GA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。 本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值啦） 并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；(GA (Simple Genetic Algorithm) is a powerful, intelligent multi-variable optimization algorithms, which mimic the breeding populations of the law to be optimized. SGA can optimize this variable, and the minimum value, maximum value (when the function of the countdown you will seek the minimum value) and to support the floating-point encoding, grey code, binary code round of gambling options, tournament selection single-point crossover, uniform Cross, floating-point crossover single point mutation, floating-point variation )

0.618算法，可以用VC运行出来，但没有MATLAB算法(function 0.618)

对灰度图像进行哈夫曼编码matlab的源代码(Huffman coding for gray image matlab source code)

神经网络训练拟合函数曲线，拟合复杂函数曲线(The neural network training function curve fitting)

一些个人收集的小波变换的程序，仅供学习参考，没有什么重要意义(Some of my personal collection of wavelet transform procedures for reference purposes only study, nothing of great significance)

BALLBEAM demonsrates Proportional-Derivative (PD) control as applied to a ball and beam simulation experiment. Execute by calling with no arguments. P and PD controllers are implemented which manipulates the beam angle in response to the position of the ball on the beam. The setpoint is indicated by the red marker and adjusted using the slider at the bottom of the figure. The control gain, Kc, and the Derivative Time constant, Td, are adjusted by a sliders located in the right hand control panel. Simulation data is stored in ballbeam.mat for subsequent analysis and parameter estimation. This demo was originally developed with Matlab 5.3 and Matlab 6.0. Some of the user interface code was shamelessly ripped out of the standard Matlab demos. The demo is now updated for Matlab 2010b with additional documentation and suggested exercises for classroom use.

EE76-Power system simulation lab

MATLAB 实现模糊聚类算法，对多位数据进行聚类划分(matlab fuzzyclustering)

这是一个序列二次规划算法的MATLAB程序，内有详细注释。(This is a sequential quadratic programming algorithm in MATLAB, with detailed notes.)

he basic idea of the method of bisection is to start with an initial interval, [a0,b0], that is chosen so that f(a0)f(b0) < 0. (This guarantees that there is at least one root of the function f(x) within the initial interval.) We then iteratively bisect the interval, generating a sequence of intervals [ak,bk] that is guaranteed to converge to a solution to f(x) = 0.