op_amp

在计算机视觉、 分割是指过程 一种数字图像划分为多个网段的 （像素为单位），也称为 superpixels 套）。图像 分割通常用来查找对象和 在图像中的边界 （线条、 曲线等）。

一个基于matlab实现的模式识别工具箱，希望给大家带来用处。(Implementation of a pattern recognition based on matlab toolbox, we hope to bring useful.)

一个萤火虫算法，很好用的。只需要换测试函数即可。不过请注意例如萤火虫个数的等参数的更换。(A firefly algorithm, easy to use. Merely need to replace the test function can be. Oh Oh Oh Oh)

是关于使用内点法来研究的最优潮流计算。对于研究opf的具有一定帮助(Interior point method to study the optimal power flow calculation. Have some help for research opf)

Diffusion-limited  Aggregation（DLA）扩散限制凝聚，是由Witten和Sander于1981年共同提出来的。其基本思想是：首先置一初始粒子作为种子，在远离种子的任意位置随机产生一个粒子使其做无规行走，直至与种子接触，成为集团的一部分。然后再随机产生一个粒子，重复上述过程，这样就可以得到足够大的DLA团簇。(Diffusion-limited Aggregation (DLA) Diffusion Limitation Cohesion was presented by Witten and Sander in 1981. The basic idea is to first set an initial particle as a seed, at any position away the seeds randomly generated a particle to make it random walk until the contact with the seeds, as part of the group. And then randomly generate a particle, repeat the process, so you can get enough large DLA clusters.)

学习matlab简单快速教程，适用于短期内掌握matlab核心数值编程(An introduction to Matlab Numerical Calculation)

岩石损伤与断裂力学一直是有限元的弱势领域，必须依赖MATLAB一类的数学软件协助，本书籍提供了基础教程与相关案例。(Rock damage and fracture mechanics have been the weak field of finite element, must rely on MATLAB class of mathematical software to help, this book provides basic tutorials and related cases.)

联合格自适应交互多模型算法。根据目标情况调整格间距。(United grid adaptive interacting multiple model algorithm.)

neural network prediction

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)