Multi-Backpropagation network: Concept and modeling - Wan Ishak, Wan Hussain (2004)
Backpropagation network is able to deal with various types of data and also has the ability to model a complex decision system. However, some problem domains might involve a large amount of data.Backpropagation network with four input units and two hidden units for example required certain epochs, to create classification or prediction model.More input units or hidden units could increase the complexity of the model and also increase its computational complexity.In other words, additional input unit or hidden unit could increase the model complexity and increase training time. This is because a larger network is more difficult to train. Like human learning, a complex problem requires certain period of time to establish learning. In some studies, backpropagation network and in general neural network has been considered "not efficient".The network, appears very time consuming even for small network architectures (Sima, 1994; Sima, 1996).Sima (1996) highlighted that the failure of effort to speed up the algorithm is caused by the network's learning complexity problem. Therefore, in this study multi-backpropagation network approach is proposed as an alternative training method for backpropagation network to reduce the complexity.