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说明：  Matlab for VQ 向量量化編碼(Matlab for VQ vector quantization)

EXAMPLE9_2_1.M 函數newelm的使用 EXAMPLE9_2_2.M 訓練Elman類神經網路 EXAMPLE9_3_1.M 函數newhop的使用 EXAMPLE9_3_2.M 創建具有兩個神經元的hopfield網路( EXAMPLE9_2_1.M : the use of fuction newelm EXAMPLE9_2_2.M : train Elman neural network EXAMPLE9_3_1.M : the use of fuction newhop EXAMPLE9_3_2.M : to create two neurons of hopfield network)

MultiScale Retinex 色彩恒常性的彩色图像增强算法(Color constancy MultiScale Retinex for Color Image Enhancement Algorithm)

程序代码说明 P0401：用Prewitt算子检测图像的边缘 P0402：用不同σ值的LoG算子检测图像的边缘 P0403：用Canny算子检测图像的边缘 P0404：图像的阈值分割 P0405：用水线阈值法分割图像 P0406：对矩阵进行四叉树分解 P0407：将图像分为文字和非文字的两个类别 P0408：形态学梯度检测二值图像的边缘 P0409：形态学实例——从PCB图像中删除所有电流线，仅保留芯片对象(Procedure Code Description P0401: detection using Prewitt operator Edge P0402: σ values with different operator LoG edge detection image P0403: detection using Canny operator Edge P0404: Image Thresholding P0405: thresholds for water line image segmentation method P0406: for the matrix decomposition quadtree P0407: text and images are divided into two categories of non-text P0408: detection of morphological gradient of the edge binary image P0409: morphological examples- from the PCB to remove all images current lines, retaining only the target chip)

说明：  这个TIMEIT Benchmark用来统计用户算法的性能，只需要将你的函数句柄传递给TIMEIT.M，它就会算出循环次数等。(This TIMEIT Benchmark users for statistical performance of the algorithm, only need to handle your function passed to TIMEIT.M, it will calculate the cycle frequency.)

本程序是一个数据预处理的程序，首先通过KL变换将数据进行一次降维，然后通过LDA对数据进一步降维(This program is a data preprocessing procedure, first by a KL transform data dimensionality reduction, then the data further by LDA dimensionality reduction)

在混响环境中，关于对室内声源的跟踪与定位。(source localization )

混沌优化，用于求解非凸优化，算法运算速度快(Chaos optimization, for solving non-convex optimization, fast algorithm)

这是一个最小方差自校正控制算法，控制器是最优控制器调节器型(This is a minimum variance self-tuning control algorithm, the controller is the optimal controller regulator type)

svm 支持向量机 有利于人更好的学习svm 以及和函数等问题(svm There are problems to better learning SVM and function etc.)