ODEPDE

几种常见混沌时间序列matlab实现 1)chua flow 2)duffing flow 3)Rossler flow 4)Lorenz flow 5)ikeda flow 6)Mackey_Glass flow 7)logistic map 8)henon map 9)Quadratic map(二次图) 也欢迎大家提供更多的混沌方程或映射的经典matlab实现。 (several common chaotic time series to achieve a Matlab) chua flow 2) duffin 3 g flow) Rossler flow 4) Lorenz flow 5) ikeda ss w 6) 7 Mackey_Glass flow) Logistic map 8) henon m ap 9) Quadratic map (Second) of welcome we provide more chaotic or mapping equations by the Code Implementation of Matlab.)

神经网络自适应竞争SOM算法，matlab算例，共9例，非常实用，可运行。(Neural network adaptive competition SOM algorithm, matlab example, a total of nine cases, very practical, you can run.)

说明：  关于matlab对通信系统建模与仿真的应用(Matlab on the communication system on the Modeling and Simulation Application)

computation TEC for ARECIBO cc to drew L

KMEANSK Performs K-means clustering given a list of feature vectors and k The argument k indicates the number of clusters you want the data to be divided into. data_vecs (N*R) is the set of R dimensional feature vectors for N data points. Each row in data_vecs gives the R dimensional vector for a single data point. Each column in data_vecs refers to values for a particular feature vector for all the N data points. The output data_idxs is a N*1 vector of integers telling which cluster number a particular data point belongs to. It also outputs centroids which is a k*R matrix, where each rows gives the vector for the cluster center. If we want to segment a color image i into 5 clusters using spacial and color information, we can use this function as follows:

用MATLAB建立的永磁同步电机仿真模型，可以用于电机的控制和驱动仿真。(Permanent Magnet Synchronous Motor MATLAB simulation model established simulation can be used to control and drive motor.)

小波神经网络,是一种很好的小波网络代码。(The wavelet neural network, the the wavelet network code is a good.)

Stochastic Models their Applications,Filter kalman

粒子群优化最优路径算法，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优(Similar to simulated annealing (SA), particle swarm optimization (PSO) optimizes the optimal path by iteratively searching for the optimal solution from a random solution. It also evaluates the quality of the solution by fitness, but it is simpler than the genetic algorithm rules. It does not have the crossover and Mutation operations of genetic algorithm. It finds the global optimum by following the best values currently searched.)

下载的一个卡尔曼语音增强程序，做了修改后给大家参考参考(Download a Kalman speech enhancement program, made modifications to your information)