Systolic array algorithm for the Hofpield neural network guaranteeing convergence
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Eun, S. | - |
| dc.contributor.author | Kim, J. S. | - |
| dc.contributor.author | Maeng, S. R. | - |
| dc.contributor.author | Yoon, H. | - |
| dc.date.accessioned | 2008-07-15T04:25:41Z | - |
| dc.date.available | 2008-07-15T04:25:41Z | - |
| dc.date.issued | 1993 | - |
| dc.identifier.citation | Electronics Letters, vol.29, no.7, pp.609-611 | en |
| dc.identifier.issn | 0013-5194 | - |
| dc.identifier.uri | http://ieeexplore.ieee.org/iel1/2220/5536/00211848.pdf | - |
| dc.identifier.uri | http://hdl.handle.net/10203/5820 | - |
| dc.description.abstract | It has been frequently reported that the Hopfield neural network operating in discrete-time and parallel update mode will not converge to a stable state, which inhibits the parallel execution of the model. In the Letter, a systolic array algorithm for the parallel simulation of the Hopfield neural network is proposed which guarantees the convergence of the network and achieves linear speedup as the number of processors is increased | en |
| dc.language.iso | en_US | en |
| dc.publisher | Institution of Engineering and Technology | en |
| dc.subject | Systolic network | en |
| dc.subject | Neural network | en |
| dc.subject | Hopfield model | en |
| dc.subject | Stability | en |
| dc.subject | Algorithm performance | en |
| dc.subject | SIMD computer | en |
| dc.title | Systolic array algorithm for the Hofpield neural network guaranteeing convergence | en |
| dc.type | Article | en |
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- CS-Journal Papers(저널논문)
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