资源列表
bpnn
- 反向传播ANN的算法实现，里面有简单的data数据demo(Backpropagation ANN algorithm implementation, there is a simple data data demo)
MINST_DEMO
- 根据概率模型实现的QDF,LDF分类算法，有minst数据集做demo(QDF, LDF classification algorithm based on probability model, MINST data set to do demo)
code
- 人工神经网络（Artificial Neural Networks，简写为ANNs）也简称为神经网络（NNs）或称作连接模型（Connection Model），它是一种模仿动物神经网络行为特征，进行分布式并行信息处理的算法数学模型(Artificial neural network is also referred to as neural network (NNs) or connection model. It is a mathematical model for simulating
code
- 单层感知器（Single Layer Perceptron）是最简单的神经网络。它包含输入层和输出层，而输入层和输出层是直接相连的。(The single layer perceptron is the simplest neural network. It contains the input layer and the output layer, and the input layer and the output layer are directly connected.)
code2
- 线性神经网络和单层感知机非常相似，输入层、输出层甚至是误差迭代函数都相同，唯一的区别就是他们的传输函数不同(Linear neural network and single-layer perceptron are very similar. Input layer, output layer and even error iteration function are all the same. The only difference is their transmission function
sql
- sql server的教学PPT 有利于初学者对于sql的一个大致了解 可以借鉴一下(The teaching PPT of SQL server is helpful for a beginner to learn a general understanding of SQL)
PDRExperiment
- 惯性走路姿势测步数，以及姿态估计侧姿势，预支手机姿态和惯性感应，手机灵活感应(The step number of the inertial walking posture, and the posture of the attitude estimation side, the mobile phone attitude and the inertial induction, the flexible induction of the mobile phone)
迭代法图像复原算法3月20日改
- 使用C#的WPF与打印机调用打印例子。使用PrintDialog 类，用打印队列实现打印。利用vc6.0+opencv实现人脸识别(Use C#'s WPF and printer to call the Print example. Use the PrintDialog class to print with a print queue. Using vc6.0+opencv to realize face recognition)
trymyown.m
- 区分Span Email和非Span Email的matlab代码，可以替换easy_ham或者hard_ham数据集。(Span Email classification Matlab)
bp神经网络
- BP神经网络用于分类，拟合所需要算法，是入门比较简单的学习算法。(BP neural network is used to classify and fit the necessary algorithm. It is a simple learning algorithm.)
chapter1
- BP神经网络对四种语音进行分类，包括网络结构初始化，网络训练，参数更新(Speech classification)
神经网络
- 本代码包括感应器神经网络、线性网络、BP神经网络、径向基函数网络(This code includes a sensor neural network, a linear network, a BP neural network, a radial basis function network)