nN

光纤光栅包层不同时，纤芯基模折射率与波长之间的关系(the relationship between the refractive index of the core fundamental mode fiber grating wavelength between cladding)

garch-copula-VaR模型用于计算投资组合风险(garch-copula-VaR model is used to calculate portfolio risk)

说明：  zernike矩的Matlab实现，可以计算任意阶矩(Matlab implementation zernike moments, moments of any order can be calculated)

黄金分割法（0.618）程序，以及进退法选择优化区间(Golden section method (0.618) procedures)

说明：  MATLAB在图形窗口中绘制或输出图形，MATLAB to draw in the graphics window or output graphics(MATLAB to draw in the graphics window or output graphics)

这是一个非常重要的而又简单的程序，是书上常用和数学建模的好帮手(great partner)

本书介绍了matlab的详细编程方法，很详细，适合初学者看看。(This book describes in detail the matlab programming method, in great detail, suitable for beginners to see.)

IEC60601-1-8音频报警标准提供了富有和声的音调，以便使它们容易定位、并能防止屏蔽的干扰。范畴与对应旋律的数量都有限，从而能获得容易掌握的不同报警顺序。另外，报警的优先权决定了报警中音符的数量，以及音符顺序的动态特征。这使得人们容易确定报警的紧急程度。这样，IEC60601-1-8标准解决了许多以前报警的限制因素，而且将有助于改善患者的安全性。(IEC60601-1-8 standard audio alarm provides a rich harmonic tone in order to make them easier to locate and prevent interference shielding. Areas and have limited the number corresponding to the melody, so that it can be easy to grasp the order of the different alarm. In addition, the alarm priority determines the number of alarm in the note, and note the order of the dynamic features. This makes it easy to determine the urgency of the alarm. This, IEC60601-1-8 standard solution before the alarm of many constraints, but will also help improve patient safety.)

FLch7BNNeg1.zip--改进的神经网络MBP算法对噪声系统辨识程序(FLch7BNNeg1.zip-- improved neural network algorithm for noise MBP system identification procedures)

说明：  SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能： 实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。 代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。 错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。 基于代理的优化：高效的全局优化（EGO）算法。 其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities: Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs. Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression. Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others). Surrogate-based optimization: efficient global optimization (EGO) algorithm. Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.)