gaussprocess

对小波X={0,1,...,10}进行一次分解与重构(Of the wavelet X = (0,1 ,..., 10) for a decomposition and reconstruction)

窄高斯随机过程的Matlab的程序有需求的自己看看那(Narrow Gaussian random process of Matlab process needs to take a look at their own)

home work: PSLR, ISLR

lte simulator without gui interface

谱估计法分析随机信号，并对其进行傅里叶变换(Spectral analysis is a tool for signal analysis. Power spectrum estimation is based on limited data looking for signals, the frequency of random process or system components. It said random signal frequency-domain statistical characteristics, there is a clear physical meaning, is an important signal processing research content. Of random signals in the frequency domain, power distribution, that is the power spectral density or power spectrum. Power spectrum estimation has been widely used.)

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)

说明：  主要都是 MATLAB RBF网络算法，可以直接使用(There are thirteen chapters in this paper. They are mainly intelligent algorithms of MATLAB, which can be used directly.)

认知无线电BPSK信号的循环平稳检测的性能仿真 包含信号循环谱图(Cognitive radio spectrum cyclostationary detection performance cycle)

matlab语言与控制系统仿真实训教程的simulink仿真文件(the matlab language with the control system simulation training tutorial simulink simulation file)

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