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SCWCD dumps for 310-083 exam for SUN certification

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)

说明：  通过matlab软件实现彩色图像直接灰度化，再进行边缘检测(Matlab software through direct grayscale color image, then the edge detection)

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burg变换，zak变换等，利用MATLAB实现，可用本程序实现对某个给定信号做对应的变换，效果良好，程序中还有关于误差的分析(burg transformation, zak transform using MATLAB to achieve, and the corresponding signal corresponding to do, transform, to good effect, as well as analysis of program errors)

基于matlab的将视频图像中的每一帧图像提取出来并保存至电脑，适用于各种视频取图(Matlab will be based on each video frame image extracted and saved to the computer for a variety of video access map)

这是一篇关于相位估计DFT算法的文章。(Frequency estimated Review)

Several Intelligent Vehicle applications require knowledge of the presence of objects in the path of the Host vehicle. Current approaches to this inpath decision process do not fully utilize the information available from the forward-looking sensor.

运用支持向量机以及模糊聚类方法对景点的大米图进行图像分割(We use the support vector machine and fuzzy clustering method to the attractions of the rice figure image segmentation)

支持向量机(SVM)用于分类的算法实现 function [D, a_star] = SVM(train_features, train_targets, params, region)(Support Vector Machine (SVM) algorithm for classification in function [D, a_star] = SVM (train_features, train_targets, params, region))