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DSM Matlab..............................

Z本程序利用BP神经网络对图像特征进行了，提取，并给出了响应的解释(Z This program makes use of BP neural network image features were extracted, and the explanation given in the response)

包含1593手写体数字0 ~ 9。从semeion.data通过MATLAB semeion.mat，可以直接使用。 每一个行为样本，其中256是手写数字的16×16，在10栏的数字识别标签，例如：如果第一行是1，然后是0号，其次是1，1。等等。 在Matlab的小例子，可以得出每一个数字，一个更好的理解。你想翻转和旋转的是写作的习惯相一致的图像。(Contains 1593 handwritten digit 0~9. from semeion.data by MATLAB semeion.mat, can be used directly. In addition of MATLAB small example, can draw each digital, a better understanding of. The image you want to flip and rotation is consistent with the habit of writing.)

shangjiao6.m为主程序；pcode.m为产生伪码的子程序；bu.m为进行补0的子程序（为了使得进行FFT和IFFT的点数满足2的N次方个） c_b.m为数据进行串并转换的子程序。(shangjiao6.m-based procedures pcode.m for producing pseudo-code subroutine bu.m for fill 0 Subroutine (carried out in order to make the point FFT and IFFT of the N-th power to meet the 2 months) c_b.m for data for string and conversion subroutine.)

说明：  利用凸包的原理,求解70个城市的旅行商问题.用Matlab实现(use of convex hull of principle, solving 70 cities in the TSP. Using Matlab)

Dijkstra最短路算法,用于求从起始点s到其它各点的最短路.可以应用到不同领域.(Dijkstra shortest path algorithm used to find the shortest path from the starting point s to the other points can be applied to different areas.)

输入参数接非线性薛定谔方程，没有标准化过的U，需要U0(Input parameters then the nonlinear Schrö dinger equation, there is no standardized over the U, needs U0)

多帧图像数据拟合，可以对多幅图片数据进行数据拟合。(Fitting multi-frame image data, image data can be multiple data fitting.)

Model Reference Adaptive Control

FCM Data set clustering using fuzzy c-means clustering. [CENTER, U, OBJ_FCN] = FCM(DATA, N_CLUSTER) finds N_CLUSTER number of clusters in the data set DATA. DATA is size M-by-N, where M is the number of data points and N is the number of coordinates for each data point. The coordinates for each cluster center are returned in the rows of the matrix CENTER. The membership function matrix U contains the grade of membership of each DATA point in each cluster. The values 0 and 1 indicate no membership and full membership respectively. Grades between 0 and 1 indicate that the data point has partial membership in a cluster. At each iteration, an objective function is minimized to find the best location for the clusters and its values are returned in OBJ_FCN.