A unifying view of some training algorithms for multilayer perceptrons with FIR filter synapses

Concerns neural network architectures for modelling time-dependent signals. A number of algorithms have been published for multilayer perceptrons with synapses described by finite impulse response (FIR) and infinite impulse response (IIR) filters (the latter case is also known as locally recurrent globally feedforward networks). The derivations of these algorithms have used different approaches in calculating the gradients, and in this paper we present a short, but unifying account of how these different algorithms compare for the FIR case, both in derivation, and performance. A new algorithm is subsequently presented. In this paper, results are compared for the Mackey-Glass chaotic time series (1977) against a number of other methods including a standard multilayer perceptron, and a local approximation method