[Michel_Verhaegen-_Vincent_Verdult]_Filtering_and

说明：  根据连续时间函数的采样数据，利用spline重构该连续函数，并检查重构误差。(spline interpolation )

The Bluetooth Voice model makes extensive use of frame based processing in Simulink, which can propagate large frames of samples at each execution step allowing for much faster simulation of digital systems.

Source code in matlab of neural network.

包括手写文字识别的各个步骤，图像的预处理、裁剪、大小转换、人工神经网络训练和识别等等。(Handwritten character recognition, including the various steps, image preprocessing, cropping, size, conversion, training and recognition of artificial neural networks and so on.)

说明：  aloha算法里的吞吐量描述123123123123(aloha algorithm described in the throughput of 123 123 123 123)

说明：  光流算法代码，实现对单运动目标，多运动目标的检测(optical flow code)

用matlab编写的简单的bp和rbf神经网路实例，有需要的下载吧，适合初学者(Written in a simple to use matlab bp and rbf neural network example, the need to download it for beginners)

Using to call in matlab

帝王蝶优化算法,一种新的元启发式优化算法！(In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn the northern United States and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called Monarch Butterfly Optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands viz. Southern Canada & northern United States (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness uations, the sum of the newly-generated but)

说明：  线性预测算法 输入：阶数p，快拍数据协方差矩阵R，观察范围内各方向导向矢量，三维矩阵phiyz。 输出：线性预测谱估计矩阵P。(Linear prediction algorithm Input: the order of p, snapshot data covariance matrix R, observe the direction in the range of steering vector, three-dimensional matrix phiyz. Output: linear prediction spectral estimation matrix P.)