pcarenlianshibie

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. )

简单的matlab程序，介绍符号函数的使用。(simple method of matlab)

求解任意阶数（大于0）的分数积分，要求输入时间数组和函数值数组(Solving any order (greater than 0) fractional integral, requiring the input time array and the array of function values)

核查的关键在于对核材料属性准确、有效地识别[1][2]。利用252Cf作为随机脉冲中子源，对其入射核材料所产生的诱发裂变中子脉冲信号进行采集、处理和分析，得到自相关、互相关、自/互功率谱、功率谱密度比等一系列参数，从而反演核材料内部的反应性的情况，便可用于分析和识别核材料，其基本原理就是252Cf中子源驱动噪声分析测量法[(The result shows that the trained Elman neural network is able to identify the characteristics of correlation function, to distinguish different concentrations, and the average recognition rate reaches 90 , with high robustness.)

说明：  武汉理工大学程磊编写的AR模型的Burg算法的matlab程序，有用(Wuhan Polytechnic University Hai prepared by the AR model Burg algorithm Matlab procedures useful)

实现了一类三涡卷混沌吸引子的同步，参考文献《一类三涡卷混沌吸引子产生与同步的研究》(To achieve a class of three-scroll chaotic attractors simultaneously, references, " a class of three-scroll chaotic attractors generation and synchronization of research" )

给予QPSK调制的awgn信道调制与解调的matlab例程(Give QPSK modulation awgn channel modulation and demodulation matlab routines)

crc编译码的过程，程序比较简单易学，已经过验证。(CRC encoding and decoding)

一种结合高斯分布的改进的QPSO,MATLAB 代码，吸引因子使用了高斯分布，加强了代码的全局搜索能力。(A combination of Gaussian distribution improved quantum particle swarm, MATLAB code, attracting factor Gaussian distribution, strengthen the global search capability of the code.)

Calculate CINR in physical layer in MATLAB. clc clear all close all N = 1 R = 10 D = sqrt(3*N)*R angleValue = [pi/6 (3*pi)/6 (5*pi)/6 (7*pi)/6 (9*pi)/6 (11*pi)/6] c = 3*10^8 carrierFreq = 900*10^6 lambda = c/carrierFreq baseHeight = 30 mobileHeight = 2 pathLossExponent = 3 shadowingValue = 0 transmitPower = 10