eight

finite difference demo code

TSP问题（又名：旅行商问题，货郎担问题）遗传算法通用matlab程序 D是距离矩阵，n为种群个数 参数a是中国31个城市的坐标 C为停止代数，遗传到第 C代时程序停止,C的具体取值视问题的规模和耗费的时间而定 m为适应值归一化淘汰加速指数,最好取为1,2,3,4,不宜太大 alpha为淘汰保护指数,可取为0~1之间任意小数,取1时关闭保护功能,建议取0.8~1.0之间的值 R为最短路径,Rlength为路径长度( TSP problem (also known as: traveling salesman problem, traveling salesman problem) genetic algorithm generic matlab program D is the distance matrix, n is the population number of parameters a C coordinates of the 31 cities to stop algebra, genetic to C on behalf of the program stops, the specific values ​ ​ of C depending on the size and time-consuming, m alpha for the fitness value of the normalized phase-out acceleration index, and is best taken as 1, 2, 3, 4, and not too much eliminated the protection index, it is desirable for any decimal between 0 and 1, taking 1:00 off the protective function, it is recommended to take a value between 0.8 to 1.0 R for the shortest path, Rlength for the path length)

预测控制MAC算法的matlab程序，多步预测，简单易懂。(Forecast control MAC algorithm matlab program, multi-step prediction, simple and easy to understand.)

利用牛顿插值蒙特卡洛法定位移动节点，画出节点运动轨迹并画出定位未知节点轨迹，最后进行误差分析(Using Newton Interpolation Monte Carlo method to locate the mobile node, the node trajectory draw and draw locate the unknown nodes track, the final error analysis)

The crf conditions with the airport is a classification model,(The crf conditions with the airport is a classification model)

说明：  射线追踪技术在5G通信领域的应用展示程序。支持多种地图的射线追踪(Application of ray tracing technology in 5g communication field. Support ray tracing of multiple maps)

说明：  通过建立药代动力学模型对比正常脑组织和癌组织的Ktrans(A comparison of Ktrans measurements obtained with pharmacokinetic models in human gliomas)

一种拟牛顿算法，非线性方程组的最优化方法，matlab调用函数(A Quasi Newton method of nonlinear equations, optimization methods, matlab function is called)

实现一些数学函数功能，比如求对角阵、单位阵的，(Implement some mathematical functions ，such as o diagonal matrix, unit array, )

It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described