gray-dynamic-model-GM-(1-1)

测试样本集对每个模式进行加噪处理，噪声值为±0.1。若网络能对加噪模式进行正确分类，则认为该网络已训练完成。(Test sample set for each pattern plus noise processing, the noise value of ± 0.1. If the network can add noise to the model correctly classified, then that the network has completed the training.)

基于遗传算法的鲁棒控制器设计及画图程序,这是对于给定的一个系统进行的设计(Genetic Algorithm Based on Robust Control Design and drawing program, this is for a given design of a system)

Graphics of PID Control of temperature

多目标的DOA估计 对多个目标的DOA来向进行跟踪，估计其来向角度等信息(DOA MUSIC estimated)

高压直流输电模型，包括由交流变换直流，直流侧发生故障(High-voltage direct current transmission model, including the communication transform dc, dc side down)

GAMS是一款强大的数学优化计算软件。本文件包含三本GAMS与MATLAB,EXCEL及其他数据库的交互的电子教程，包括：Interfacing_with_GAMS.pdf；Linking_Matlab_and_GAMS_A_Supplement.pdf；MATLAB_and_GAMS.pdf(GAMS is a powerful mathematical optimization software. This document contains three electronic tutorial GAMS interact with MATLAB, EXCEL and other s, including: Interfacing_with_GAMS.pdf Linking_Matlab_and_GAMS_A_Supplement.pdf MATLAB_and_GAMS.pdf)

采用一种快速收敛变步长LMS（Least mean square ） 自适应最小均方算法matlab源程序，其中算法所做的工作是用FIR 滤波器的预测系统，对IIR系统进行预测，如果阶数越高越能逼近被预测系统。(Using a fast convergence of variable step size LMS (Least mean square) adaptive least mean square algorithm matlab source, one of algorithm is the work done by FIR prediction filter systems, prediction of IIR systems, if the higher order the more it can be prediction system approximation.)

图像分割算法之间的比较，比较前沿的放发，利用了kmeans和模糊K均值(Between the image segmentation algorithm compares the release cutting-edge hair, use a K-means and fuzzy kmeans)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

CC_ABSCISSA returns the I-th Clenshaw Curtis abscissa.