Deterministic nonmonotone strategies for effective training of multilayer perceptrons

We present deterministic nonmonotone learning strategies for multilayer perceptrons (MLPs), i.e., deterministic training algorithms in which error function values are allowed to increase at some epochs. To this end, we argue that the current error function value must satisfy a nonmonotone criterion...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2002-11, Vol.13 (6), p.1268-1284
Hauptverfasser:	Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Backpropagation algorithms Convergence Error functions Information systems Iterative algorithms Learning Mathematics Minimization methods Multilayer perceptrons Neural networks Numerical analysis Strategy Training
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