shortest-path

模拟退火算法的基本思想是从一给定解开始,从邻域中随机产生另一个解,接受Metropolis准则允许目标函数在有限范围内变坏,它由一控制参数t决定,其作用类似于物理过程中的温度T,对于控制参数的每一取值,算法持续进行“产生—判断—接受或舍去”的迭代过程,对应着固体在某一恒定温度下的趋于热平衡的过程,当控制参数逐渐减小并趋于0时,系统越来越趋于平衡态,最后系统状态对应于优化问题的全局最优解,该过程也称为冷却过程,由于固体退火必须缓慢降温,才能使固体在每一温度下都达到热平衡,最终趋于平衡状态,因此控制参数t经缓慢衰减,才能确保模拟退火算法最终优化问题的整体最优解。(Simulated annealing algorithm based on the idea that a given solution from the beginning, randomly generated from the adjacent domain to another solution, Metropolis acceptance criteria for the objective function to allow a limited range of deterioration, which is a control parameter t decided that its role is similar to the physical the course of the temperature T, the control parameters for each value, the algorithm continued to "have- to determine- to accept or rounding" of the iterative process, corresponding to a solid at a constant temperature of the process tends to thermal equilibrium, when control parameters and tended to decrease gradually 0:00, the system equilibrium state more and more, and finally the system state corresponds to the optimization problem of global optimal solution, the process is also known as the cooling process must be slow as a result of annealing temperature of solid to make solid temperature in each of them had attained thermal equilibrium, and ultim)

在matlab环境中利用遗传算法解决旅行商问题。(In the matlab environment, the use of genetic algorithm to solve the traveling salesman problem.)

fastkurtogram是法国人编辑的程序，可以用他快速进行故障诊断(fastkurtogram Frenchman editing program, with his rapid fault diagnosis)

说明：  一个简单的BP神经网络分类声发射信号的范例，对理解框架有帮助，不过结果不够严谨，不利于发表(A simple example of BP neural network classification of AE signals is helpful to understand the framework, but the results are not rigorous enough to be published)

该代码主要是实现自适应功能，用Matlab编写！(The code is mainly the realization of adaptive function, using Matlab to prepare!)

用已知的空间两点求解声束穿过去界面的声束传播路径非线性算法。(Known spatial two o' clock solve the acoustic beam to pass through to the interface of the sound beam propagation path algorithm.)

使用直接模拟蒙特卡罗法的Matlab编程，里面三个算例，如湖面积、资产路径等的概率求解法～(Vincent Leclercq, The MathWorks, 2007 vincent.leclercq@mathworks.fr Ths is the set of files (with the powerpoint presentation, in french or in english) used for the Webinar "Simulation de Monte Carlo en MATLAB". - The first demo (LakeArea, run MainLakeArea) is computing the size of a polyogon using a MC approach - The second demo (PortSim, run WebinarScript) can do 2 things: First, we genrate some equity paths, to verify the lognormality If changing the mode to OptionPricing (uncomment one of the first line), then those spaths will be used for pricing an asian option - the 3rd Demo,in myMC (run MonteCarlo.m) , show how to simulate some corelated asset paths - The 4th demo, (run VanillaPricingUsingDifferentMethods.m in the VarReduction folder) , wil compare th results obtrtain byt differents reduction of Variance technics or "quasi" 礛ont Carlo simultion using Hamlton and sobol Sequences)

模糊步长LMS算法及其性能分析，希望有帮助(Fuzzy step-size LMS algorithm and its performance analysis)

十个关于基本函数测试的函数编程。关于MatLAB的函数编程(the ten function about basemarched try in matlab)

数学建模清华数模讲义\model10b (Mathematical modeling Qinghua digital-analog handouts model10b)