Radial-Basis-Function-Neural-Controller-for-Tanke

Extended Kalman Filter MATLAB code

初学者必备 完整的MATLAB入门到精通书本辅助实验程序 学后基本可以上手(Complete beginners MATLAB entry to the master book assisted experimental program learn basic can get started)

求电磁场边值，程序简洁，方法得当，运算速度快，是这一程序的最突出的特点(For electromagnetic field boundary value, the program is simple and methodologically sound, computing speed, this procedure is the most prominent feature of)

用灰度线性变化的方法进行图像增强，包含代码及代码注释(Using grayscales linear method of image enhancement, contain code and code comments)

使用人工神经网络识别手写字符，有样本有结果，代码可运行(handwriting character recognize with NN)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

小车-倒立摆的平衡控制问题，由强化学习方法来求解，参考sutton《强化学习》一书(cart-pole balancing control problem，reinforcement learning algortihm is used here， please refer to Sutton s book ,named 《reinforcement learning》)

说明：  通过对TSP问题的优化建模利用蚁群算法求解TSP文图(Using ant colony algorithm to solve TSP problem by optimizing the model of TSP problem)

LMS algorithm PIL cosimulation for DM642 EVM.

La logique floue est le meilleur moyen de convertir les stratégies de contrô le du langage naturel qui sont utilisées par l’homme à une forme utilisable par les machines. Des expériences ont montré qu’un contrô leur classique donne parfois même de meilleurs résultats que l’opérateur humain