differential-Detection-for-diversity

通过剪力弯矩的方程，计算混凝土框架结果的强度和刚度极限(frame)

matlab仿真非相干信号宽带MUSIC方位估计，与DAS和MVDR方法进行对比。信号到达角度可调整(Incoherent broadband signal simulation matlab MUSIC DOA estimation, comparison with the DAS and MVDR methods. Signal reaches the angle adjustable)

自抗扰控制器扩张状态观测器代码，离散模型(ADRC ESO code)

一维边值问题线性有限元数值解法的matlab程序。有详尽的算法分析，程序注释清楚。适用于有限元方法的初学者，能帮助读者加深对基本概念的理解。(one-dimensional linear boundary value problems finite element numerical solution of Matlab procedures. A detailed analysis of algorithms, clear procedures for the Notes. Application of the finite element method in the beginners will help readers to better understand the basic concepts.)

用matlab模拟光线在三棱镜中的轨迹，以及顶角与折射方向的关系(stimulated ray trace in triple prism )

求解DEA　CCR BCC 超效率 matlab程序(Solve the DEACCR BCC super efficiency matlab program)

power system particle swarm optimization

灰度图像边缘检测中的prewitt算法的MATLAB仿真实现(MATLAB simulation grayscale image prewitt edge detection algorithm to achieve)

5位0-7数字的rs编码，解码和纠错，两位纠错码(Rs numbers 0-7 5 encoding, decoding, and error correction, the two error correction code)

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.)