matlab11

这是一个混沌理论中重要的Lorenz映射的程序。通过曲线很容易看到奇妙的混沌现象(Chaos Theory important Lorenz mapping procedures. Curve it is easy to see the amazing phenomenon of Chaos)

说明：  回声状态网络模型算法实现，回归算法采用脊回归算法(Esn model algorithm implementation)

基础的音频处理matlab程序，设计巴特沃斯低通数字滤波器实现滤波。(Matlab-based audio processing procedures, Butterworth low-pass digital filter designed to achieve the filtering.)

说明：  时间序列分析中参数估计方面的实现,在matlab环境下实现的代码(parameter estimation)

标准大气模型，使用，matlab编写，可用于航空航天等相关领域(The model of the standard atmosphere)

说明：  遗传算法及说明 遗传算法及说明。(Genetic algorithm and describe genetic algorithms and description.)

S函数编写的自抗扰控制器，已经试验过，没有问题(ADRC controller is written by S function, it has been tested and there is no problem.)

重要的模拟程序，模拟精度很高，值得大家好好参考，好好学习一下(Important simulation, the simulation accuracy is high, is worth a good reference to learn about)

The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multirecombination (the (=I )-SA-ES) using isotropic mutations is investigated on the general elliptic objective function. An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES system by a set of difference equations. The solutions of this system are used to analytically calculate the optimal learning parameter  . The theoretical results are compared and validated by comparison with real (=I )-SA-ES runs on typical elliptic test model cases. The theoretical results clearly indicate that using a model-independent learning parameter  leads to suboptimal performance of the (=I )-SA-ES on objective functions with changing local condition numbers as often encountered in practical problems with complex fitness landscapes.

克里金法广泛地应用于地下水模拟、土壤制图等领域，是一种很有用的地质统计格网化方法。它首先考虑的是空间属性在空间位置上的变异分布．确定对一个待插点值有影响的距离范围，然后用此范围内的采样点来估计待插点的属性值。该方法在数学上可对所研究的对象提供一种最佳线性无偏估计(某点处的确定值)的方法。它是考虑了信息样品的形状、大小及与待估计块段相互间的空间位置等几何特征以及品位的空间结构之后，为达到线性、无偏和最小估计方差的估计，而对每一个样品赋与一定的系数，最后　进行加权平均来估计块段品位的方法。(Kriging is a technique that provides the Best Linear Unbiased Estimator of the unknown fields (Journel and Huijbregts, 1978 Kitanidis, 1997). It is a local estimator that can provide the interpolation and extrapolation of the originally sparsely sampled data that are assumed to be reasonably characterized by the Intrinsic Statistical Model (ISM). An ISM does not require the quantity of interest to be stationary, i.e. its mean and standard deviation are independent of position, but rather that its covariance function depends on the separation of two data points)