disflowtry

基于burg算法实现的AR模型功率谱计算，算法已经仿真通过，有前向预测误差及后向预测误差，非常适合与学习加深现代谱估计的理论 (Burg algorithm to achieve the AR model power spectrum calculation algorithm has been through simulation, the forward prediction error and prediction error is ideal for learning to deepen modern spectral estimation theory)

说明：  自编灰色预测MATLAB程序，可供初学者使用(The grey prediction matlab program is compiled by ourselves, which can be used by beginners)

基于MATLAB对图形图像进行多方面的处理，先图形直方图均衡处理，再通过积分投影，分别为水平投影，垂直投影，再则进行精确定位目标(MATLAB-based graphical images on a wide range of processing, the first graphical histogram equalization processing, and through the integral projection, horizontal projection, vertical projection, she then accurately locate targets)

Hidden markov random filed

An Uncorrelated Variable Step-size LMS Adaptive Algorithm 1Qi Zhang, 2Yuancheng Yao, 3 Mingwei Qin

用介电常数法计算减反膜（ARC）对[300-1200]纳米各波长光的减反效果(This function calculates reflections of antireflection coating (ARC) bewteen 300-1200nm wavelength)

the test code of cnn net of deep learning and the test example in matlab(the test code of cnn net of deep learning and the test example)

用matlab的各种编程例子，可以供大家更好的理解掌握MATLAB，自己觉得挺有用的。(Various matlab programming examples for all to better understand and grasp the MATLAB feel quite useless.)

智能算法中的遗传算法，解决多层编码车间调度问题(The genetic algorithm in intelligent algorithm solves the problem of multilayer coding shop scheduling.)

说明：  梯度下降法是一個一階最佳化算法，通常也稱為最速下降法。 要使用梯度下降法找到一個函數的局部極小值，必須向函數上當前點對應梯度（或者是近似梯度）的反方向的規定步長距離點進行疊代搜索。如果相反地向梯度正方向疊代進行搜索，則會接近函數的局部極大值點；這個過程則被稱為梯度上升法。(The gradient descent method is a first-order optimization algorithm, also commonly referred to as the steepest descent method. To find the local minimum of a function using the gradient descent method, an iterative search must be performed to the specified step distance point in the opposite direction of the gradient (or approximate gradient) of the current point on the function. If the search is reversed in the opposite direction to the positive direction of the gradient, it will approach the local maximum point of the function; this process is called the gradient ascent method.)