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All the current Ann neural network algorithm DaquanOverview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2 Advantages and Limitations3 Sensor Neural Network3.1 Main

Http://blog.sina.com.cn/s/blog_98238f850102w7ik.htmlAll the current Ann neural network algorithm Daquan(2016-01-20 10:34:17)reproduced Tags: it Overview1 BP Neural network1.1 Main functions1.2 Advantages and

Transfer from http://www.cnblogs.com/heaad/archive/2011/03/07/1976443.htmlThe main contents of this paper include: (1) Introduce the basic principle of neural network, (2) Aforge.net the method of realizing Feedforward neural Network, (3) Matlab to

Tricks efficient BP (inverse propagation algorithm) in neural network trainingTricks efficient BP(inverse propagation algorithm) in neural network training[Email protected]Http://blog.csdn.net/zouxy09tricks! It's a word that's filled with mystery

Content Summary:(1) introduce the basic principle of neural network(2) Aforge.net method of realizing Feedforward neural network(3) the method of Matlab to realize feedforward neural network---cited Examples  In this paper, fisher's iris data set is

Artificial neural Network (Artificial Neural Network, Ann) is a hotspot in the field of artificial intelligence since the 1980s. It is also the basis of various neural network models at present. This paper mainly studies the BPNN model. What is a

At present, deep learning (Deepin learning, DL) in the field of algorithm is rounds, now is not only the Internet, artificial intelligence, the life of the major areas can reflect the profound learning led to the great change. To learn deep learning,

A reference to the artificial neural network should think of three basic knowledge points: One is the neuron model, the other is the neural network structure, and the third is the learning algorithm. There are many kinds of neural networks, but the

bp neural network in BP for back propagation shorthand, the earliest it was by Rumelhart, McCelland and other scientists in 1986, Rumelhart and in nature published a very famous article "Learning R Epresentations by back-propagating errors ". With

http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training