The negative transfer problem in neural networks: a solution

Abstract

The authors introduce a modified BP (backpropagation) model that can be used in sequential learning to overcome the NET (negative transfer) effect. Simulations were conducted to contrast the performance of the original BP model with the modified one. The results of the simulations showed that effect of the NT can be completely eliminated, and in some cases reversed, by using the modified BP model. The behavior and interactions of the weight matrices are studied over successive training sessions. This work confirms the need to have an overall cognitive architecture that goes beyond the basic application of the learning model.<>