Neural network ensembles and their application to traffic flow prediction in telecommunications networks

It is well-known that large neural networks with many unshared weights can be very difficult to train. A neural network ensemble consisting of a number of individual neural networks usually performs better than a complex monolithic neural network. One of the motivations behind neural network ensembles is the divide-and-conquer strategy, where a complex problem is decomposed into different components each of which is tackled by an individual neural network. A promising algorithm for training neural network ensembles is the negative correlation learning algorithm which penalizes positive correlations among individual networks by introducing a penalty term in the error function. A penalty coefficient is used to balance the minimization of the error and the minimization of the correlation. It is often very difficult to select an optimal penalty coefficient for a given problem because as yet there is no systematic method available for setting the parameter. This paper first applies negative correlation learning to the traffic flow prediction problem, and then proposes an evolutionary approach to deciding the penalty coefficient automatically in negative correlation learning. Experimental results on the traffic flow prediction problem will be presented.