Matlab-Newton-Interpolation-Code

PSO optimization exa

performs BPSK modulation and correlation demodulation

Low density parity check ....

单层感知器分类和权值调整过程，学习率分别为0.01 0.05 0.1 0.5(Single layer perceptron device classification and weights to adjust the process)

无线通信中MIMO信道的空时编码技术之V-Blast编码(Wireless communication MIMO channel coding of space-time coding technology V-Blast)

motor simulation using Simulink software for beginners in drives

This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.

多元线性回归及显著性检验Matlab程序（完美版）(Multiple linear regression and significance tests Matlab program (perfect version))

The MatLab code uses lattice Boltzmann (LB) method to simulate two phase flows for immiscible fluids (blue & red fluids) in 2D according to the D2Q9 scheme. The model is also referred to as color model or chromodynamic model or ILB (immiscible LB). This MatLab implementation of ILB saves an AVI file in dir_avi= C: that records frames generated by the time evolution iterations. The code implements in D2Q9 the model originally developed by Gunstensen [Gunstensen AK, Rothman D. Lattice The MatLab code uses lattice Boltzmann (LB) method to simulate two phase flows for immiscible fluids (blue & red fluids) in 2D according to the D2Q9 scheme. The model is also referred to as color model or chromodynamic model or ILB (immiscible LB). This MatLab implementation of ILB saves an AVI file in dir_avi= C: that records frames generated by the time evolution iterations.(The MatLab code uses lattice Boltzmann (LB) method to simulate two phase flows for immiscible fluids (blue & red fluids) in 2D according to the D2Q9 scheme. The model is also referred to as color model or chromodynamic model or ILB (immiscible LB) . This MatLab implementation of ILB saves an AVI file in ' ' dir_avi = ' C: ' ' ' that records frames generated by the time evolution iterations The code implements in D2Q9 the model originally developed by Gunstensen [Gunstensen AK, Rothman D.. Lattice The MatLab code uses lattice Boltzmann (LB) method to simulate two phase flows for immiscible fluids (blue & red fluids) in 2D according to the D2Q9 scheme. The model is also referred to as color model or chromodynamic model or ILB (immiscible LB .) This MatLab implementation of ILB saves an AVI file in ' ' dir_avi = ' C: ' ' ' that records frames generated by the time evolution iterations.)

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.)