A Method for Task Allocation in Modular Neural Network with an Information Criterion

Abstract

It is well known that large-scale neural networks suffer from serious problems such as the scale problem and the local minima problems. Modular architecture neural network is an approach to alleviate these problems. It is important that the construction of modular neural network is the selection or construction of a network that can converge and has the good generalization ability for a task, and the Akaike Information Criterion (AIC) is a criterion of evaluation of estimated model from observed parameters is a very useful tool for selection of network. This paper proposes a method for task allocation in a modular architecture neural network. The method allocates a best fit network that has a good generalization ability from multiple neural networks for a task with (AIC) and the state of convergence of a network, simply and certainly. The performance of proposed method is evaluated with the Fisher’ Iris data and the What and Where vision tasks.