Design and stability of Hopfield associative memory
| buir.advisor | Morgül, Ömer | |
| dc.contributor.author | Savran, M. Erkan | |
| dc.date.accessioned | 2016-01-08T20:09:39Z | |
| dc.date.available | 2016-01-08T20:09:39Z | |
| dc.date.issued | 1991 | |
| dc.description | Ankara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1991. | en_US |
| dc.description | Thesis (Master's) -- Bilkent University, 1991. | en_US |
| dc.description | Includes bibliographical references leaves 38-40. | en_US |
| dc.description.abstract | This thesis is concerned with the selection of connection weights of Hopfield neural network model so that the network functions as a content addressable memory (CAM). We deal with both the discrete and the continuous-time versions of the model using hard-limiter and sigmoid type nonlinearities in the neuron outputs. The analysis can be employed if any other invertible nonlinearity is used. The general characterization of connection weights for fixed-point programming and a condition for asymptotic stability of these fixed points are presented. The general form of connection weights is then inserted in the condition to obtain a design rule. The characterization procedure is also employed for discrete-time cellular neural networks. | en_US |
| dc.description.provenance | Made available in DSpace on 2016-01-08T20:09:39Z (GMT). No. of bitstreams: 1 1.pdf: 78510 bytes, checksum: d85492f20c2362aa2bcf4aad49380397 (MD5) | en |
| dc.description.statementofresponsibility | Savran, M Erkan | en_US |
| dc.format.extent | viii, 40 leaves | en_US |
| dc.identifier.uri | http://hdl.handle.net/11693/17364 | |
| dc.language.iso | English | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Hopfield neural network | en_US |
| dc.subject | Content addressable memory | en_US |
| dc.subject | Fixedpoint programming | en_US |
| dc.subject | Cellular neural networks | en_US |
| dc.subject.lcc | QA76.87 .S28 1991 | en_US |
| dc.subject.lcsh | Neural networks (Computer science). | en_US |
| dc.subject.lcsh | Machine theory. | en_US |
| dc.title | Design and stability of Hopfield associative memory | en_US |
| dc.type | Thesis | en_US |
| thesis.degree.discipline | Electrical and Electronic Engineering | |
| thesis.degree.grantor | Bilkent University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | MS (Master of Science) |
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