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无线室内定位中精确定位算法，chan算法。希望能给你带来帮助(Wireless indoor positioning precise positioning algorithm, chan algorithm. I hope you will help)

ppt for basic channel estimation

实现了蚁群算法求解TSP问题。注释详细 function[R_best,L_best,L_ave,Shortest_Route,Shortest_Length]=ACATSP(C,NC_max,m,Alpha,Beta,Rho,Q) ------------------------------------------------------------------------- 主要符号说明 C n个城市的坐标，n×2的矩阵 NC_max最大迭代次数 m蚂蚁个数 Alpha表征信息素重要程度的参数 Beta表征启发式因子重要程度的参数 Rho信息素蒸发系数 Q信息素增加强度系数 R_best各代最佳路线 L_best各代最佳路线的长度 ========================================================================= (Ant Colony Algorithm for the realization of the TSP problem. Notes the detailed function [R_best, L_best, L_ave, Shortest_Route, Shortest_Length] = ACATSP (C, NC_max, m, Alpha, Beta, Rho, Q) ------------------------------------------------------------------- The main symbol------ C n that cities coordinates, n × 2 matrix NC_max the largest number of iterations m the number of Alpha ant pheromones characterized the importance of the parameters Beta factor heuristic importance of characterization of the parameters Rho evaporation coefficient of pheromone pheromone Q increase in intensity coefficient R_best best route generations generations L_best the length of the best route ====== ================================================== =================)

对3阶滤波器LSL算法的权系数和MSE进行预测(Coefficient and MSE rights of the order filter LSL algorithm to predict)

自适应递推最小二乘RLS算法，用matlab的m文件(Adaptive recursive least squares RLS algorithm)

支持向量机,C++与Matlab，测试可通过(Support Vector Machine)

MATLB神经网络代码，创建一个向前的BP网络进行训练( network program, which has very detailed notes, very suitable for the general neural network and artificial intelligence for beginners)

Matlab源程序代码，用于拟合超二次曲线，曲面的代码，呵呵，与大家一起分享受(Matlab source code for the quadratic curve-fitting, surface code Oh, and that they have enjoyed.)

abaqus二次开发子程序umat符号说明及实例详解，初学者必备之基础(abaqus secondary development subroutine umat symbol descriptions and examples detailed essential foundation for beginners)

To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm. (To estimate the input-output mapping with inputs x and outputs y generated by the following nonlinear, nonstationary state space model: x (t+ 1) = 0.5x (t)+ [25x (t )]/[( 1+ x (t)) ^ (2)]+ 8cos (1.2t)+ process noise y (t) = x (t) ^ (2)/20+ 6 squareWave (0.05 (t-1 ))+ 3+ time varying measurement noise using a multi-layer perceptron (MLP) and both the EKF and the hybrid importance-samping resampling (SIR) algorithm.)