MCMJM96A

求连续函数的卷积，利用matlab中的conv函数实现(Seek a continuous function of the convolution, using the conv function in Matlab to achieve)

说明：  基于QR分解的求根MUSIC的被动定位方法(ROOT MUSIC LOCALIZATION BASED ON QR )

说明：  基于股票分析方法的电力系统负荷预测：主要思路是从过去的负荷中得出对应的DIF值、DEA值、MACD值。然后再通过DIF去估测明天DIF估计值、DEA估计值、MACD估计值。然后又通过DEA、MACD估计值算出一个新的DIF估计值。再将这两个DIF估计值算术平均得出一个新的DIF估计值，然后反推出负荷的预测值，这样就完成了负荷预测。(The main idea is drawn from the past load values corresponding to the DIF, DEA value, MACD value. And then estimate DIF ,DEA,MACD in tomorrow use the past DIF , DEA , MACD. Then through the DEA, MACD calculate a new estimate of DIF. Then the arithmetic average of the two estimated DIF derived a new estimate of DIF, and then introduce predictive value of load, so that is the completion of the load forecast. )

Simple Filters: Echo and Reverb

music algorithm - comunication

说明：  本软件利用matlab开发的一个具有交互界面的软件，主要功能为RSA的加密、解密功能，简单易懂，富有详细代码(This software is developed using a matlab interface software, the main function for the RSA encryption and decryption functions, easy to understand, full of detailed code)

做过模糊PID的朋友很多遇到过这种问题，就是仿真启动不起来，停留在e-7等地方或者仿真很慢，自次上次有个朋友说改变步长能解决这个问题，最后的曲线能达到满足要求的时候，我让他找个例子他就没有后文，以前我做了很多这样的模糊PID，但靠改变步长并没有解决问题，仿真曲线直接发散出去了，我自己这两天也总在思考这个问题，通过不断的调试总结，明白了不少，给遇到这个问题的朋友讲解一下解决办法吧，以一个例子详解：(Done a lot of friends of the fuzzy PID encountered this problem, simulation start up, stay in the e-7 and other local or simulation is very slow, have a friend who said to change the step size to solve this problem since the times the last, the finalcurve to achieve to meet the requirements when I asked him to find examples of his later, I have done a lot of fuzzy PID, but by changing the step does not solve the problem, the simulation curve directly divergence out, Itwo days always thinking about this problem, summarized by the debug and understand a lot of friends to experience this problem to explain the solution, an example Detailed:)

使用Matlab对一集体函数进行fft计算，并绘制复制图像，并减小栅格效应(Using Matlab fft function on a collective basis, and draw copy the image and reduce the effect of the grid)

作者写得还是很简练的，尤其是对新手，我就是看他的熟悉Matlab的。(It is practical for the beginners to start use the Matlab.)

Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21, 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions.