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New strategy for adaptively constructing multilayer feed-forward neural networks

It is quite well-known that one-hidden-layer feed-forward neural networks (FNNs) can approximate any continuous function to any desired accuracy as long as enough hidden units are included. Due to this fact many developments in constructive neural networks have been concentrated on only constructive or adaptive one-hidden-layer FNNs. However, this fact does not necessarily imply that one-hidden-layer networks are the most efficient and the best network structure feasible, as one has no explicit guideline to properly select the network structure. Consequently, in practice it has been observed that networks with more than one hidden layer may perform better than the one-hidden- layer networks in some applications. In this paper, we propose a novel strategy for constructing a multi-hidden- layer FNN with regular connections. The new algorithm incorporates in part the policy for adding hidden units from a one-hidden-layer constructive algorithm, and has in part its own new policy for additional layer creation. Extensive simulations are performed for nonlinear noisy regression problems, and it is found that the proposed algorithm converges quite fast and produces networks with one or as many hidden layers/units as required, which are dictated by the complexity of the underlying problem.