FEM-structural-analysis

经典的压缩感知重建算法，可以很方便的使用。(it good)

混沌与分岔，验证混沌的初值敏感性，logisitic的周期和分岔(Chaos and bifurcation, the initial value to verify the sensitivity of chaos, logisitic the cycle and bifurcation)

利用matlab作图画出氦氖激光器的增益曲线(Matlab plot to draw the gain curve of the He-Ne laser)

核主成分分析方法是主成分分析的改进算法，其采用非线性方法提取主成分(Kernel principal component analysis method is an improved algorithm of principal component analysis, which uses a nonlinear principal components extracted)

有较好的参考价值，自己编的5种调制信号，仿真效率很高的。( There are good reference value, Own five modulation signal, High simulation efficiency.)

有关聚类的所有方法的原理的讲解，重要的是有相应的程序，我运行过，都非常好用，而且用到的标准数据测试都有。(Clustering methods on the principle of all explanation, it is important there is a corresponding procedure, I ran, are very easy to use, and have used the standard data test.)

Source code simulating the simplest communication protocol - Stop and Wait.

MATLAB SIMULATION FOR CUK CONVERTER IEEE

主成分分析(Principal Component Analysis, 简称PCA)是一种常用的基于变量协方差矩阵对信息进行处理、压缩和抽提的有效方法。 (The principal component analysis (Principal Component Analysis, PCA) is a common covariance matrix of information processing, compression and extraction.)

本书是The IMA Volumes in Mathematics and its Applications系列的一本，由Springer-Verlag发表，用于交流每年数学规划算法方面的新成果，希望对于广泛的科学团体有帮助。 许多工程，运筹，和科学应用包括离散或连续的决策变量，以及这些决策变量的非线性关系。这种 Mixed-integer nonlinear programming （MINLP，混合整数非线性规划），兼具来自非线性问题的非凸函数优化，以及来自整数的离散优化，这两方面的挑战。这种问题是优化问题里面建模最灵活的类型之一，但是因为它范围极广，在最一般的情况下，它是几乎无法解的。然而，日渐壮大的研究者和程序员队伍，包括化学工程，运筹研究者，工业工程师，机械工程师，经济学家，统计学家，计算机科学家，运营经理，和数学程序员，都对求解大规模MINLP问题产生兴趣。(Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners ― including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers ― are interested in solving large-scale MINLP instances.)