sanleilvboqi

MATLAB神经网络30个案例分析—案例28 灰色神经网络的预测算法—订单需求预测源代码及数据(MATLAB Neural Network 30 Case Studies- Case of 28 gray neural network prediction algorithm- order demand forecast source code and data)

水文趋势、突变点分析的matlab相关程序——spearman、kendall、pettitt法(Hydrological trends point mutation analysis matlab procedures- spearman, kendall, pettitt France)

sprt code in matlab. sprt can isolate the change of variance in a signal.

用matlab来解决隐马尔科夫的概率问题(HMM in matlab)

元智大学matlab stateflow培训教材(stateflow introduction for you)

同步电机的交直轴自感的计算方法，可用于dq模型的同步电机的计算和研究。(AC synchronous motor direct axis inductance calculation method can be used to model calculations and research dq synchronous motor.)

这是一本关于车辆动力学方面的书籍，主要针对车辆工程与车辆控制相关的人员学习使用(This is a book about the dynamics of the vehicle, mainly for vehicle engineering or vehicle control research staff to learn to use)

bat algorithm source code

摘　要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)

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)