Modified constructive backpropagation for regression
Choosing a network size is a difficult problem in neural network modelling. In many recent studies constructive or destructive methods that add or delete connections, neurons, layers have been studied for solving this problem. In this work, we consider the constructive approach, which is in many cases computationally a very efficient approach. In particular, we address the construction of feedforward networks by the use of modified constructive backpropagation. The proposed modification is shown to be important especially in regression-type problems like time series modelling. Indeed, according to our time-series prediction experiments, the proposed modified method is competitive in terms of modelling performance and training time compared to the well-known cascade-correlation method.
constructive backpropagation structure learning feedforward neural networks