资源列表
cgnxw
- High simulation efficiency, Modeling and simulation pwm rectifier In the MATLAB image texture feature.
jengganlan
- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. The entire training process BP neural network.
A算法实现
- 基于人工智能中的A算法，编程并解决旅行商问题(Based on the A algorithm in artificial intelligence, programming and solving traveling salesman problem)
Bpnet
- 梯度下降法BP神经网络程序，可读取CSV文件(Gradient descent BP neural network program, can read CSV file)
opencvCar
- 对车牌进行图像处理，包括字符分割处理，字符识别处理(License plate for image processing, including character segmentation, character recognition processing)
SVMcgForClass.m
- 二分类，参数寻优 matlab 支持向量机(Two classification)
GA
- 遗传算法工具箱用于一维二维寻优示例matlab程序(The genetic algorithm toolbox for one and two-dimensional optimization example program)
JSvmLib
- 用java实现的svm用来理解svm非常不错(this is svm source use java)
第10章 模糊逼近算法
- RBF网络的学习过程与BP网络的学习过程类似，两者的主要区别在于各使用不同的作用函数。BP网络中隐层使用的是Sigmoid函数，其值在输入空间中无限大的范围内为非零值，因而是一种全局逼近的神经网络；而RBF网络中的作用函数是高斯基函数，其值在输入空间中有限范围内为非零值，因为RBF网络是局部逼近的神经网络。(The learning process of RBF networks is similar to the learning process of BP networks. The mai
7788
- FMCW frequency modulated continuous wave radar range and angular measurements, Is a good learning materials to learn PCA feature extraction, Some adaptive signal processing algorithms.
KNN
- 最近邻学习算法，Python实现，最近邻规则分类(steps: In order to determine the unknown instance categories, with examples of all known categories as reference Parameter selection of K The calculation examples and all known examples of the unknown distance Choose the
01DTree
- 步骤： 为了判断未知实例的类别，以所有已知类别的实例作为参照 选择参数K 计算未知实例与所有已知实例的距离 选择最近K个已知实例 根据少数服从多数的投票法则(majority-voting)，让未知实例归类为K个最邻近样本中最多数的类别(steps: In order to determine the unknown instance categories, with examples of all known categories