Example of an artificial neural network algorithm implemented by Python [Based on the back propagation algorithm], python Artificial Neural Network
This example describes the artificial

Recently in the study of Artificial neural network (Artificial neural netwroks), make notes, organize ideas
Discrete single output perceptron algorithm, the legendary MP
Two-valued Network: The value of the independent variable

1 Introduction
Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying.
But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The ma

1 Introduction
An XOR operation is a commonly used calculation in a computer:
0 XOR 0 = 0
0 XOR 1 = 1
1 XOR 0 = 1
1 XOR 1 = 0
We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural network learning ability is limited ,

In the previous article "Artificial Neural Network (Artificial neural netwroks) Notes-Eliminate the sample order of the BP algorithm" to modify the weight of the method is called the "steepest descent method." Every time the weight of the changes are determined, the weight w

Single-layer perceptron does not solve the XOR problem
Artificial Neural Networks (Artificial neural netwroks) have also fallen into low ebb due to this problem, but the multilayer Perceptron presented later has made the artificial neur

This is an extension of the discrete single output perceptron algorithm
Related symbolic definitions refer to the artificial neural network (Artificial neural netwroks) Note-discrete single output perceptron algorithm
Ok,start our Game
1. Initialization weight matrix W;

Artificial neural Network (Artificial neural netwroks) Notes--2.1.3 steps in the discrete multi-output perceptron training algorithm are multiple judgments, so we say it's a discrete multiple output perceptron.
Now take the formula Wij=wij+α (YJ-OJ) Xi instead of that step

introduces the latter.1958 Rosenblatt presented the Perceptron (Perceptron), which is essentially a linear classifier, 1969 Minsky and Papert wrote a book "Perceptrons", which they pointed out in the book: ① Single-layer perceptron can not achieve XOR function, ② computer ability is limited, can not deal with the long-running process of neural

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

Introduction of artificial neural network and single-layer network implementation of and Operation--aforge.net Framework use (v)The previous 4 article is about the fuzzy system, it is different from the traditional value logic, the theoretical basis is fuzzy mathematics, so some friends looking a little confused, if in

Open source Artificial Neural Network Computing Library FANN Learning Note 1These days machine learning is very fire, neural network is the machine learning algorithm is a more important one. This time I also took some effort, learned a little fur, by the way to do some stud

development period, in this period, the neural network has received international attention, each country has carried on the research, forms the neural network development another climax.Artificial neural Network (ANN) is a kind

around Microsoft "Xiaoice" the Dog has a "fog" (ie difficult to understand), until this year 2 Month 6 Microsoft technology Executive Sun Jian published an article confirming that the "Xiaoice Dog" feature is Microsoft's proprietary "Artificial Neural network" ( ANN to the vast number of users to provide a network serv

Artificial neural network is a simulation of the biological nervous system. Its information processing function is determined by the input and output characteristics (activation characteristics) of the network Unit (neuron), the topology of the network (the connection mode o

The ① Artificial Neural Network (ANN) is a widely connected giant system. Neuro-scientific research shows that the main part of the human central nerve cortex is composed of 10[11]~10[12] neurons, each neuron has a 10[1]~10[5] synapse, Synapse is a junction between neurons, determining the strength and nature of the connection between neurons. This suggests that

Sample program Download: Http://files.cnblogs.com/gpcuster/ANN3.rarIf you have questions, please refer to the FAQIf you do not find a satisfactory answer, you can leave a message below:)0 CatalogueIntroduction to Artificial neural network (1)--application of single-layer artificial

Artificial neural Network (ANN) is a mathematical model for information processing, which is similar to the structure of synaptic connection in the brain, in which a large number of nodes (or neurons) are connected to form a network, that is, "neural

The Artificial Neural network (Artificial neural Networks), as its name implies, is modeled after the neuron structure of the NPC. is an artificial neural

do?Similar to the "Xiaoice to know the dog" scene knowledge is existent, and many, has the universal significance. To this end, scientists start the "fantasy" habit, want to use electronic components to build "artificial neural network" (ann), instead of answering the waiter in the brain of the neuron (neurons) network

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