Optimal approximate computation of Euclidean distance in spiking neural P systems framework
Abstract A fast approximation for the Euclidean distance, i.e., the $$L_2$$ -norm of a complex number was investigated. The problem is obtaining an initial estimate with minimal effort and maximum numerical precision. We show how to eliminate the square root computation and improve the precision of...
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| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02793-3 |
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| author | Otgonbayar Agvaan Gordon Cichon Uuganbaatar Dulamragchaa Hyun-chul Kim Seonuck Paek Tseren-Onolt Ishdorj |
| author_facet | Otgonbayar Agvaan Gordon Cichon Uuganbaatar Dulamragchaa Hyun-chul Kim Seonuck Paek Tseren-Onolt Ishdorj |
| author_sort | Otgonbayar Agvaan |
| collection | DOAJ |
| description | Abstract A fast approximation for the Euclidean distance, i.e., the $$L_2$$ -norm of a complex number was investigated. The problem is obtaining an initial estimate with minimal effort and maximum numerical precision. We show how to eliminate the square root computation and improve the precision of an approximation with just a few shifts and additions from 41% to just 4%. This corresponds to an improvement of precision from 1 bit to almost 5 bits. As successive Newton iterations double the number of bits, this eliminates the need for two initial iterations. For example, a result with 16-bit precision can be obtained by just two iterations instead of four. We implement the computation by using two types of basic neurons, namely a high-pass (HP) neuron and a low-pass (LP) neuron, in the Spiking Neural P systems framework. |
| format | Article |
| id | doaj-art-289f33ee342644cf8bb9ace15292bddc |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-289f33ee342644cf8bb9ace15292bddc2025-08-20T03:08:40ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-02793-3Optimal approximate computation of Euclidean distance in spiking neural P systems frameworkOtgonbayar Agvaan0Gordon Cichon1Uuganbaatar Dulamragchaa2Hyun-chul Kim3Seonuck Paek4Tseren-Onolt Ishdorj5Department of Computer Science, School of Information and Communication Technology, Mongolian University of Science and TechnologyDepartment of Computer Science, School of Information and Communication Technology, Mongolian University of Science and TechnologyInstitute of Mathematics and Digital Technology, Mongolian Academy of SciencesDepartment of Software, Sangmyung UniversityDepartment of Software, Sangmyung UniversityDepartment of Computer Science, School of Information and Communication Technology, Mongolian University of Science and TechnologyAbstract A fast approximation for the Euclidean distance, i.e., the $$L_2$$ -norm of a complex number was investigated. The problem is obtaining an initial estimate with minimal effort and maximum numerical precision. We show how to eliminate the square root computation and improve the precision of an approximation with just a few shifts and additions from 41% to just 4%. This corresponds to an improvement of precision from 1 bit to almost 5 bits. As successive Newton iterations double the number of bits, this eliminates the need for two initial iterations. For example, a result with 16-bit precision can be obtained by just two iterations instead of four. We implement the computation by using two types of basic neurons, namely a high-pass (HP) neuron and a low-pass (LP) neuron, in the Spiking Neural P systems framework.https://doi.org/10.1038/s41598-025-02793-3Euclidean distance$$L_{2}$$ -normapproximate computationSpiking neural P systemsHP/LP neurons |
| spellingShingle | Otgonbayar Agvaan Gordon Cichon Uuganbaatar Dulamragchaa Hyun-chul Kim Seonuck Paek Tseren-Onolt Ishdorj Optimal approximate computation of Euclidean distance in spiking neural P systems framework Scientific Reports Euclidean distance $$L_{2}$$ -norm approximate computation Spiking neural P systems HP/LP neurons |
| title | Optimal approximate computation of Euclidean distance in spiking neural P systems framework |
| title_full | Optimal approximate computation of Euclidean distance in spiking neural P systems framework |
| title_fullStr | Optimal approximate computation of Euclidean distance in spiking neural P systems framework |
| title_full_unstemmed | Optimal approximate computation of Euclidean distance in spiking neural P systems framework |
| title_short | Optimal approximate computation of Euclidean distance in spiking neural P systems framework |
| title_sort | optimal approximate computation of euclidean distance in spiking neural p systems framework |
| topic | Euclidean distance $$L_{2}$$ -norm approximate computation Spiking neural P systems HP/LP neurons |
| url | https://doi.org/10.1038/s41598-025-02793-3 |
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