FNNPSOGSA

matlab编程，实现对视频中运动人体的检测，代码简单易懂(matlab programming, video detection of the human body in motion, the code is easy to understand)

双基地声纳定位精度GDOP与目标方位的关系，目标方位为与过基线中点法线的夹角。(Bistatic sonar positioning accuracy GDOP relationship with the target position, the target azimuth angle between the baseline and over the midpoint of normals.)

遗传算法工具包，可利用遗传算法的编程，减少大量工作量。(Genetic algorithm toolkit can be programmed by genetic algorithms to reduce the large workload.)

matlab函数参考，包括各种常用的函数开发工具，功能很好很强大啊(matlab function reference, including a variety of functions commonly used development tools, features a very good strong ah)

说明：  基于ZF和MMSE预均衡线性预编码，还有其结果的比较。(ZF and MMSE pre-equalization based on linear pre-coding, as well as the results of the comparison.)

Solaris 2.6以后，SUN删除了版本号中的"2"，因此，SunOS 2.10就叫做Solaris 10。 Solaris的早期版本后来又被重新命名为Solaris 1.x(After Solaris 2.6 , SUN deleted version number "2" , therefore , SunOS 2.10 is called Solaris 10. Earlier versions of Solaris later was renamed Solaris 1.x )

分析非平稳时变的实证分解法（EMD）的基础上产生的适应性，本征模函数（IMF）的数据，处理的数据。(analyze the non-stationary time-varying data processed by the Empirical Decomposition Method (EMD), which generates the adaptive basis, Intrinsic Mode Functions (IMF), from the data. Each chapter package name (or directory) is specified in parentheses. )

matlab程序 五点滑动平均方法 很好很强大(matlab program five-point moving average method)

stuff for the serial modem on ELSA cards

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )