Neural networks: the state of the art

Artificial neural networks (ANNs) have demonstrated their success in many applications due to their ability to solve some problems with relative ease of use and the model-free property they enjoy. ANNs can solve problems without the need to understand or learn the analytical and statistical properties of the problem nor the solution steps. Research in ANNs has resulted in a variety of models and learning algorithms. In this paper, a brief review of recent advances in the field is presented. The paper then focuses on the recent work conducted by the author's group on modular neural networks. In particular, the paper discusses the different modular structures, modes of interactions, capabilities, co-operation among modules and fusion of their decisions. Performance of these models has proven to be superior to nonmodular neural networks.