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Neural Network Model of Associative Memory: To Visualize Solutions in Weight Space
| dc.contributor.author | Imada, A. | ru |
| dc.coverage.spatial | Brest | ru |
| dc.date.accessioned | 2021-07-27T08:09:32Z | |
| dc.date.available | 2021-07-27T08:09:32Z | |
| dc.date.issued | 1999 | |
| dc.identifier.citation | Imada, A. Neural Network Model of Associative Memory: To Visualize Solutions in Weight Space / A. Imada // International Conference on Neural Networks and Artificial Intelligence ICNNAI’99 = Международная конференция по нейронным сетям и искусственному интеллекту ICNNAI’99 : Proceedings, Brest, Belarus, 12–15 October 1999 / Brest Polytechnic Institute, Department of Computers and Laboratory of Artificial Neural Networks, Belarus Special Interest Group of International Neural NetWork Society, International Neural NetWork Society, Belarusian State University of Informatics and Radioelectronics (Belarus), Belarusian Academy of Sciences, Institute of Engineering Cybemetics (Belarus), Universidad Politechnica de Valencia (Spain), Institute of Computer Information Technologies (Ukraine, Ternopil) ; ed. V. Golovko. – Brest : BPI, 1999. – P. 26–31. | ru |
| dc.identifier.uri | https://rep.bstu.by/handle/data/20665 | |
| dc.description.abstract | We apply some variants of evolutionary computations to the Hopfield model of associative memory. In the model, a number of patterns can be stored in the network as attractors if synaptic weights are determined appropriately. One of our goals of this study is to learn the number and distribution of these Solutions in weight space, which is still an open problem. To address this issue, we test a method to visualize Solutions in high-dimensional space in this paper. | ru |
| dc.language.iso | en | ru |
| dc.publisher | BPI | ru |
| dc.title | Neural Network Model of Associative Memory: To Visualize Solutions in Weight Space | ru |
| dc.type | Научный доклад (Working Paper) | ru |
