An adaptive least squares algorithm for the efficient training of artificial neural networks

Kollias, S; Anastassiou, D

dc.contributor.author	Kollias, S	en
dc.contributor.author	Anastassiou, D	en
dc.date.accessioned	2014-03-01T02:40:54Z
dc.date.available	2014-03-01T02:40:54Z
dc.date.issued	1989	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30266
dc.subject	Artificial Neural Network	en
dc.subject	Least Square	en
dc.title	An adaptive least squares algorithm for the efficient training of artificial neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/31.192419	en
heal.identifier.secondary	http://dx.doi.org/10.1109/31.192419	en
heal.publicationDate	1989	en
heal.abstract	A novel learning algorithm is developed for the training of multilayer feedforward neural networks, based on a modification of the Marquardt-Levenberg least-squares optimization method. The algorithm updates the input weights of each neuron in the network in an effective parallel way. An adaptive distributed selection of the convergence rate parameter is presented, using suitable optimization strategies. The algorithm has better	en
heal.journalName	IEEE International Symposium on Circuits and Systems	en
dc.identifier.doi	10.1109/31.192419	en