The main motivation for developing the software was to evaluate practically the suggested model. For bigger neural networks it is very difficult to analyze the behaviour of the system theoretically, in particular the generalization performance. One way to demonstrate that the model is working is to run a simulation. This does not formally prove that the proposed architecture has certain properties because only a finite number of examples can be simulated. Nevertheless this methodology is widely used in the field of neural networks. From a more practical point of view a successful simulation does `prove' that the architecture is capable of solving a certain type of problem.
In this section aspects of the implementation are discussed, including the structure of the software for the modular neural network, the usage of the programs and the interfaces to the developed library. The implementation also included mechanisms for preprocessing of the data.