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sliding mode:In this project, a sliding mode controller is designed to a linear plant with non-linear dis- turbances. The performance of the designed controller is then veried via simulation using MATLAB and Simulink. Then, the sign function in the designed controller will be replaced by a sat function, and the performance of the new controller will be investigated. Finally, some conclusion remarks will be made according to the simulation results.

虽然奇异值分解（SVD）和特征值分解（EVD的）是行之有效的，可以通过先进的设施设备先进的算法，它不是通常所说，有一个内在的迹象，可以显着影响的不确定性的结论计算及诠释来自其结果。我们提供一个解决方案，标志模糊的问题确定了从奇异向量的内积和个人数据载体签署奇异向量的迹象。该数据可能有不同的载体，但它有它自身的定位和实际意义的选择方向，其中多数的向量点。这可以通过评估发现了内心的签署标志产品的总和。(Although the Singular Value Decomposition (SVD) and eigenvalue decomposition (EVD) are well-established and can be computed via state-of-the-art algorithms, it is not commonly mentioned that there is an intrinsic sign indeterminacy that can significantly impact the conclusions and interpretations drawn from their results. We provide a solution to the sign ambiguity problem by determining the sign of the singular vector from the sign of the inner product of the singular vector and the individual data vectors. The data vectors may have different orientation but it makes intuitive as well as practical sense to choose the direction in which the majority of the vectors point. This can be found by assessing the sign of the sum of the signed inner products. )

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国外一本有关线性反馈控制分析与设计的书籍，用Matlab开发的。(A book titled Linear Feedback Control Analysis and Design With Matlab .)

交互式选取生长起始点，将感兴趣部分分割出来。(Select starting point for interactive growth, will be interested in part from it.)

一个简单实用的预测控制matlab程序，对初学者有很大的帮助(A simple and practical predictive control matlab program, a great help for beginners)

multidevice antenna for portable device

读取硬盘SMART值,很详细的源代码。很有参考学习的价值(read hard smart value)

训练集/测试集产生 load spectra_data.mat 随机产生训练集和测试集 temp = randperm(size(NIR,1)) 训练集——50个样本 P_train = NIR(temp(1:50),:) T_train = octane(temp(1:50),:) 测试集——10个样本 P_test = NIR(temp(51:end),:) T_test = octane(temp(51:end),:) N = size(P_test,2) 数据归一化 ( Training set/test set generation load spectra_data.mat randomly generated training set and test set temp = randperm (size (NIR, 1)) training set- 50 samples P_train = NIR (temp (1:50) ,:)' T_train = octane (temp (1:50 ),:)' test set- 10 samples P_test = NIR (temp (51: end ),:)' T_test = octane (temp (51 : end ),:)' N = size (P_test, 2) Data Normalization)

基于改进差分演化算法的石油层评价指标的特征选择(Feature improved oil layer evaluation based on differential evolution algorithm choice)