Asirvadam, Vijanth S. and McLoone, Sean F. and Irwin, George W.
Computationally efficient sequential
learning algorithms for direct link
Neurocomputing, 69 (1-3).
Computationally efficient sequential learning algorithms are developed for direct-link
resource-allocating networks (DRANs). These are achieved by decomposing existing recursive
training algorithms on a layer by layer and neuron by neuron basis. This allows network
weights to be updated in an efficient parallel manner and facilitates the implementation of
minimal update extensions that yield a significant reduction in computation load per iteration
compared to existing sequential learning methods employed in resource-allocation network
(RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate
a pruning strategy to control network growth, are evaluated on three different system
identification benchmark problems and shown to outperform existing methods both in terms
of training error convergence and computational efficiency.
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