Fuzzy neural networks

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dc.creatorGuven, Murat
dc.date.accessioned2016-11-14T23:11:14Z
dc.date.available2011-02-19T00:59:41Z
dc.date.available2016-11-14T23:11:14Z
dc.date.issued1998-12
dc.degree.departmentMathematicsen_US
dc.description.abstractSince the development of computer technology, methods have been developed and investigated to mimic the processes of the human brain. The human brain is a collection of billions of neurons interconnected with each other. Interconnected neurons are modeled with artificial neural networks (ANNs or NNs). Neural networks, mathematically speaking, are a system of linked parallel equations that are solved simultaneously and iteratively. Initial research can be found in papers by McCulloch-Pitts (1943), Hebb (1949), Rosenblatt (1958), Minsky-Papert (1969), and Hopfield (1982). Since 1982, research into neural networks has exploded and the use of neural networks to solve complex nonlinear problems has expanded (from pattem recognition to actual learning to playing games). Many different neural network architectures (the feedforward network, CMAC, Hopfield network, Kohonen network) have been developed to aid in the solution of these problems. In this paper, we are interested in the feedforward network.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2346/22451en_US
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.rights.availabilityUnrestricted.
dc.subjectArtificial intelligenceen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectFuzzy setsen_US
dc.subjectFuzzy algorithmsen_US
dc.titleFuzzy neural networks
dc.typeThesis

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