Artificial neural networks (ANN) as simulators and emulators-an analytical overview

Abstract

Because of their ability to exploit the tolerance for imprecision and uncertainty in real-world problems, and their robustness and parallelism, artificial neural networks (ANNs) and their techniques have become increasingly important for modeling and optimization in many areas of science and engineering. As a consequence, the market is flooded with new, increasingly technical software and hardware products. This paper presents an analytical overview of the most popular ANNs, both in hardware and software modes. After an overview of ANN, the paper discusses global optimization for ANN training, and the NOVEL hybrid method is presented and its performance is discussed. The paper then discusses the techniques and means for parallelizing neurosimulations of ANNs, both at a high programming level and at a low hardware-emulation level. It then presents vector microprocessor architectures and the Spert II fixed-point system as applied to multimedia and human-machine interface. Finally, it introduces the most recently explored concept of cellular neural networks (CNN), its performance and operation are analyzed. Conclusions and recommendations conclude the paper.