GA sample program

各种数值算法，包括：二叉树、贪心算法、搜索算法、遗传算法、模拟退火算法、分治策略等等。-A variety of numerical algorithms, including: binary tree, greedy algorithm, search algorithm, genetic algorithm, simulated annealing algorithm, sub-rule tactics and so on.

BP神经网络C＋＋实现-BP neural network implemented by C++

hide markov model(HMM) for matlab including initiate,train,and test three parts -hide markov model (HMM) for Matlab includi Vi initiate, train, and test three parts

载入txt文件迷宫，含几种搜索方法，例如depth first search, breadth first search等-maze included txt file containing several search methods, such as depth first search, breadth first search, etc.

BP神经网络的C语言实现 BP神经网络解决异或问题 canny源代码 HMM的C语言实现 isodata K-MEANS 车牌识别系统 矢量量化的C语言实现 -Neural Network C language BP neural network solution differences or problems canny source HMM C language isodata K-MEANS License Plate Recognition System Vector Quantization C Language

利用动量梯度下降算法训练BP网络，得到误差显示图，并最终进行预测-Gradient descent algorithm using momentum BP network training, the error display map, and, ultimately, to predict

Kohonen网络的学习过程可描述为：对于每一个网络的输入，只调整一部分权值，使权向量更接近或更偏离输入矢量，这一调整过程就是竞争学习。随着不断的学习过程，所有输入矢量都在输入矢量空间相互分离，形成了各自代表输入空间的一类模式，这就是Kohonen网络的特征自动识别的聚类功能。请解压缩后按照readme提示进行操作。-Kohonen network learning process can be described as follows : for each one network input, only part of adjusting the weights, weight vector closer to or further from the input vector, the adjustment process is competitive learning. With the continuous process of learning, all the input vector in the importation of vector space separate form their own representatives importation of a type of space, and this is a Kohonen network of automatic clustering functions. Please decompress after readme according to instructions.

可以实现快速的模拟退火算法，很有价值，-can achieve rapid simulated annealing and of great value, thank you

该程序是基于BP网络的PID控制系统。系统由两部分构成：经典的PID控制器和神经网络。-that the procedure was based on BP network PID control system. System consists of two parts : the classic PID controller and neural networks.

基于梯度法编写的RBF神经网络程序，实现对输入数据的逼近-Gradient method based on the preparation process of the RBF neural network to achieve the approximation of the input data