Hybrid learning in a multi-neural network architecture

This paper describes a new class of neural networks (multiple feedforward networks (MFFNs)) obtained by integrating two feedforward networks in a novel manner. A new multiple backpropagation (MBP) algorithm that can be seen as a generalization of the backpropagation (BP) algorithm is also presented. The MFFNs and MBP algorithm together form a new neural architecture that is in most cases preferable to the use of multilayer perceptron networks trained with the BP algorithm. Experimental results on benchmarks show that the advantages offered by the new architecture are shorter training times for online learning and better generalization and function approximation capabilities.