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