Improving backpropagation learning under limited precision
Most of the work to date on learning in neural networks assumes the availability of unlimited precision for implementing weights and the neural transfer functions. Recently some studies have investigated the effects of limited precision on the learning and generalisation performance. In this paper, we identify the causes of performance degradation due to limited precision and propose a novel algorithm to improve the performance of Backpropagation.
limited precision modified bp minima