RF_matlab

说明：  基于MATLAB的组合模型 在港口吞吐量预测中的应用 以某港1998--2007年集装箱吞吐量为实测数据，建立其集装箱吞吐量的灰色理论、三次指数平滑、三次多项式 等预测模型，在MATLAB下，对比该港集装箱吞吐量各模型预测拟合值与实际值的差异。分析了差异产生的原因及其单一预 测模型的局限性，提出了港口集装箱吞吐量组合预测法。其预测误差明显低于其它单模型。运用组合预测模型．可以降低误 差。提高预测精度。(A combination of MATLAB-based model in port throughput prediction of a Hong Kong 1998- 2007 container throughput for the measured data, the establishment of its container throughput of the gray theory, the three exponential smoothing, prediction models, such as cubic polynomial in MATLAB, the contrast of the Hong Kong container throughput of fitting the models to predict the value of the difference between the actual value. Analysis of the causes of differences and the limitations of a single prediction model, the combination of port container throughput prediction. Its prediction error was significantly lower than other single model. The use of combination forecasting model. Can reduce the error. Improve the prediction accuracy.)

Matlab 课程设计,Matlab 课程设计,Matlab 课程设计(Matlab )

detect face of people with matlab

小波包分析提取振动信号中的特征频率，仿真效率很高的，采用累计贡献率的方法。( Wavelet packet analysis to extract vibration signal characteristic frequency, High simulation efficiency, The method of cumulative contribution rat.)

companion routine to LUfactor

分数阶傅立叶变换，可用于LFM信号的相干源信号的估计(Fractional Fourier Transform can be used for LFM signal of the coherent source signal estimate)

c++的adc（数模转换器）测试实例程序，很实用，适合初学者(the c++ adc test instance procedures, very practical, very classic)

Tensor image sensing source code. very intuitive. also contains the tensor toobox.

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)

产生m序列的源代码，为5级线性反馈移位寄存器，有兴趣的可以看看(Generate the source code of the m-sequence, five linear feedback shift register, interested can look at)