A framework for grid-based neural networks

The usage of neural networks in critical systems is fast approaching the norm. In such systems, the issue of accuracy is of prime importance. Ensemble methods have commonly been used to increase accuracy but at the cost of training time and computational requirements. With the advent of grid computing, it is expected that these problems are solved. This paper proposes a framework in which an ensemble of neural networks is implemented over a grid-based environment.