MuiltyTool

通过混沌理论系统产生混沌序列，用三种不同的方法产生二值序列。(System through chaos theory chaotic sequence, using three different methods of binary sequences.)

bark to freq transformation

Heat equation with variable coeficients using Succesive Over Relaxation

emd的c语言版本，方便使用，高效率！！！！！！！！！！！(emd program is edited by the c language ,and it s easy to use and efficiently .it will give you a good impression !!!!)

This a set of simple implementations for aligning scans of range-bearing laser data. They are written as demonstrations of basic algorithm implementations and are not designed for real-world (ie, efficient) application. The source code consists of MatLab scripts, mex-files and C++ code. to objects). This demo also provides a nice example of using "placement new" and explicit destruction. (This a set of simple implementations for aligning scans of range-bearing laser data. Th are written as a journalist in demonstrations of basic algor ithm implementations and are not designed for r Leveraging-world (ie, efficient) application. The source code consi sts of MatLab scripts, Mex-files and C code. to objects). This demo als o provides a nice example of using the "placement ne w "and explicit destruction.)

在LEACH算法的基础上对簇头选择从节点剩余能量与节点分布位置进行了加权处理，仿真结果显示，较LEACH协议延长了网络生存时间，降低了网络总体能耗。(On the basis of the LEACH algorithm for cluster heads selection on the basis of the node residual energy and the node distribution location weighted processing, simulation results show that, the LEACH protocol extended the network survival time, reduce the network energy consumption as a whole. )

此程序描写了一个猜数过程，系统在给定范围内随机生成一个数，如何根据提示在较少的次数内才对这个数。(This procedure describes a guessing process, the system randomly generated within a given range of a number of tips on how to lower the number of times the number of fishes.)

DFA是一种分析去趋势波动分析方法的matlab程序，被广泛用来作为理论基础。(DFA is a detrended fluctuation analysis to analyze the matlab program, is widely used as a theoretical basis.)

插值拟合方法在matlaB中的实现，压缩包里面包含讲义和源代码。(Interpolation fitting method in MATLAB to achieve compression package which contains lecture notes and source code.)

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.