可以编译的神经网络控制算法代码

LVQ神经网络的分类——乳腺肿瘤诊断 (LVQ neural network classification- breast cancer diagnosis)

多种策略变权重系数的粒子群优化算法源代码，用于求解复杂函数极值问题。(Multiple strategies variable weight coefficient of particle swarm optimization algorithm source code, is used to solve complex function extremum problems.)

MUSIC算法是基于天线阵列协方差矩阵的特征分解类DOA估计算法，这里给出的是基于解相干的MUSIC算法。(MUSIC algorithm is based on the antenna array covariance matrix eigen-decomposition DOA estimation algorithm presented here is based on the MUSIC algorithm decoherence.)

有序抖动也是一种常用的半色调技术方法，可以说是最简单的领域处理过程的方 法。它是用一个固定大小的阈值矩阵在原始灰度图像上面一边“移动”，一边比较。如 果原始图像中像素的灰度值大于相应阈值矩阵中的阈值，那么抖动后该像素的值为1 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报

Solucion problema de la mochila con busqueda tabu.

matlab算法中第8章内容,其他部分陆续上传中，请稍后(matlab algorithm in Chapter 8, other parts of the succession Uploading, please wait)

说明：  关于ofdm系统中ml检测信号的算法，供大家参考(ML detection algorithm for OFDM system)

阵列信号处理，均匀圆阵模式空间算法，，通过虚拟阵元进行ＤＯＡ估计-Array signal processing, uniform circular array pattern space algorithm, through the virtual array element to the estimated DOA

说明：  基于数学形态学的彩色细胞图像分割 可以提取出背景较复杂的细胞图像(Based on mathematical morphology color cell image segmentation)

病态线性方程组的计算题，涉及Gauss消元法、雅可比迭代法、高斯-赛德尔迭代法、最速下降法和共轭梯度法。每一个方法，都编写一个m文件，封装成函数的形式。然后通过总的HilbLineEquSet.m文件来调用执行，画出误差曲线图，得到运行结果。总的Matlab程序流程，如下所示： 病态方程组的计算包括：HilbLineEquSet.m、gauss.m、jacobi.m、gauss_seidel.m、fastest_descend.m和conjugated_grad.m六个文件。 程序执行结果包括：求解结果、迭代次数、迭代误差数据、误差曲线图等。 (Morbid Linear Equations calculation problems involving Gauss elimination method, Jacobi' s iterative method, Gauss- Seidel iterative method, steepest descent method and conjugate gradient method. Each method, all the preparation of an m file, packaged in the form of a function. Then the total HilbLineEquSet.m file to invoke the implementation of draw error curve, to be running results. General Matlab program flow, as follows: the calculation of morbid equations include: HilbLineEquSet.m, gauss.m, jacobi.m, gauss_seidel.m, fastest_descend.m and conjugated_grad.m six documents. Procedures for implementation of the findings include: solving a result, the number of iterations, iterative error data, error curve and so on.)