Biologically inspired models to train neural networks
A major stumbling block to the successful implementation of neural networks for nonlinear regression models is overtraining. This paper presents two models to combat overtraining, based on the biological concept that the strength of the connection between neurons develops over time. For the problems investigated, the models produce smooth solutions with no sign of overtraining. For the practical Diminishing Returns problem, the Error Constraints Model, because of its mathematical formulation, determines the minimum number of hidden layer neurons. As a result, the present work is important because, if an acceptable level of error can be specified, then the Error Constraints Model can be used to determine the network architecture. Copies of the workbook implementations of all the models presented in this paper may be downloaded from the author's website.
overtraining feedforward neural network learning algorithms neural network architecture optimisation pruning