NSK, an Object-Oriented Simulator Kernel for Arbitrary Feedforward Neural Networks

Abstract

An object-oriented neural network simulator kernel is presented. It is based on a general mathematical model for arbitrary feedforward nets. We propose a C++ implementation of this model which satisfies the following requirements : expandability (allowing an easy implementation of a new neural model), portability and efficiency (the kernel does not increase significantly computation times for classic models, compared to a direct object-oriented implementation). Learning algorithms such as gradient-based ones can be written for arbitrary nets and are therefore directly available for every particular model. 1 Introduction Due to the lack of mathematical theories about the capabilities of neural networks, the discovery of interesting properties of these networks strongly relies on computer simulations. One of the main practical problems we have to deal with when trying to experiment a new network model is the development of an implementation of this model in order to conduct experimenta..