Before we examine various libraries and tools available for constructing neural networks, we will implement our own basic neural network using standard Java libraries. The next example is an adaptation of work done by Jeff Heaton (http://www.informit.com/articles/article.aspx?p=30596). We will construct a feed-forward backpropagation neural network and train it to recognize the XOR operator pattern. Here is the basic truth table for XOR:
| X | Y | Result |
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
This network needs only two input neurons and one output neuron corresponding to the X and Y input and the result. The number of input and output neurons needed for models is dependent upon the problem at hand. The number of hidden neurons ...
