Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, CHen_US
dc.contributor.authorLiu, HLen_US
dc.contributor.authorLin, CTen_US
dc.date.accessioned2014-12-08T15:43:48Z-
dc.date.available2014-12-08T15:43:48Z-
dc.date.issued2001-06-01en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://dx.doi.org/10.1109/3477.931548en_US
dc.identifier.urihttp://hdl.handle.net/11536/29602-
dc.description.abstractThe stability analysis of the learning rate for a two-layer neural network (NN) Is discussed first by minimizing the total squared error between the actual and desired outputs for a set of training vectors. The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found fur this two-layer NN, It has also been proven in this paper that the dynamic stable learning rate for this two-layer NN must be greater than zero. Thus it is guaranteed that the maximum error reduction can be achieved by choosing the optimal learning rate for the next training iteration. A dynamic fuzzy neural network (FNN) that consists of the fuzzy linguistic process as the premise part and the two-layer NN as the consequence part is then illustrated as an immediate application of our approach, Each part of this dynamic FNN has its own learning rate for training purpose. A genetic algorithm is designed to allow a more efficient tuning process of the two learning rates of the FNN. The objective of the genetic algorithm is to reduce the searching time by searching for only one learning rate, which is the learning rate of the premise part, in the FNN. The dynamic optimal learning rates of the two-layer NN can he found directly using our innovative approach, Several examples are fully illustrated and excellent results are obtained for the model car backing up problem and the identification of nonlinear first order and second order systems.en_US
dc.language.isoen_USen_US
dc.subjectbackpropogationen_US
dc.subjectfuzzy neural networksen_US
dc.subjectgenetic algorithmen_US
dc.subjectlearning rateen_US
dc.titleDynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/3477.931548en_US
dc.identifier.journalIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICSen_US
dc.citation.volume31en_US
dc.citation.issue3en_US
dc.citation.spage467en_US
dc.citation.epage475en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000169597800022-
dc.citation.woscount36-
Appears in Collections:Articles


Files in This Item:

  1. 000169597800022.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.