Ken-Soft

Here is yet another simple Neural Network that implements Back-Error Propagation as a form of Reinforced Learning.
The entire project is written in C++ and requires no special libraries to compile and run.
main.cpp contains code to train both a 2 and 3-input Logical AND gate.
The zipped source code can be downloaded here
A Linux executable is already compiled and included in the zip, but feel free to recompile it. A Code::Blocks Project file is also included.

To Compile
g++ *.cpp -o NeuralNetwork
It will output an executable name “NeuralNetwork”
To Run
open a terminal and type:
./NeuralNetwork
Sample Output
Neural Network Connections Inited
Trained in 10000 trails within an error of 1.03127e-05
0 & 0 = 4.63117e-05
0 & 1 = 0.00349833
1 & 0 = 0.00290835
1 & 1 = 0.995469
Train Logical AND 2 Inputs Demo End
Neural Network Connections Inited
Training...
Trained in 5584 trails within an error of 9.99977e-06
0 & 0 & 0 = 3.62242e-05
0 & 0 & 1 = 0.00194301
0 & 1 & 0 = 0.000102096
0 & 1 & 1 = 0.00344352
1 & 0 & 0 = 0.000142368
1 & 0 & 1 = 0.00333881
1 & 1 & 0 = 0.0035418
1 & 1 & 1 = 0.993633
Logical AND 3 Inputs Demo End


Resources:
About Neural Networks (English)
About Neural Networks (Japanese/日本語)
Java Implementation of a Neural Network

This Neural Network is a command line implementation that uses the Back-Error Propagation learning algorithm. The main difference with this model is that all train data is loaded from an external XML file.
The Train data specification can be found Here.

The Zip file containing the Jar file and traindata directory can be downloaded here: NN.jar
Below is the Syntax for running NN02.jar in a command line:
java -jar NN02.jar [num center layers] [num center layer neurons] [learning rate]
An example includes:
java -jar NN02.jar 1 50 1.5
Which specifies a neural network with 1 center layers, 50 neurons per center layer, and a learning rate of 1.5. If now parameters are supplied it runs with default parameters.
Once executed, it should give a display similar to the image below.

The neural network Source is almost unchanged from a previous example found Here
Included in the zip file is my custom, and very very messy, XML Parser. If you plan on using any of this code for any real development, I recommend using a standard XML Parser.