shuixiangshuiweimohukongzhi

与SQL 数据进行交互操作的代码文件 matlab条用数据库 (Code files interoperate with SQL data)

截取一个小的窗口对于目标序列图像进行跟踪和锁定(Interception of a small window for the target image sequence tracking and locking)

含噪语音的语音增强，包换多种算法的去噪效果对比，最后集中在一个操作界面上。(Speech enhancement in noisy speech, noise effect contrast to replacement of several algorithms, finally concentrated in one operating interface.)

对于蜂窝移动通信模型的仿真，有很强的可移植性(Cellular mobile communication model for the simulation, there is a strong portability)

a program to find the edges of particles in an image.

MOTOR CHARACTERISTICS

　计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸 特征提取方法。(Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method.)

差分进化算法DE，及检验函数Goal。5个检验函数用来验证DE算法的有效性。(Differential evolution algorithm DE, and test function Goal. The five test functions are used to verify the effectiveness of the algorithm of DE.)

locked system with digital code

径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)