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STRIP - a strip-based neural-network growth algorithm for learning multiple-valued functions
We consider the problem of synthesizing multiple-valued logic functions by neural networks. A genetic algorithm (GA) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V...
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| Published in: | IEEE transaction on neural networks and learning systems 2001-03, Vol.12 (2), p.212-227 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c488t-b6b02f550f7dc2118a46b7300605d3f4625ebc839d74fd26e07f7b25abc05ffb3 |
| container_end_page | 227 |
| container_issue | 2 |
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| container_title | IEEE transaction on neural networks and learning systems |
| container_volume | 12 |
| creator | Ngom, A. Stojmenovic, I. Milutinovic, V. |
| description | We consider the problem of synthesizing multiple-valued logic functions by neural networks. A genetic algorithm (GA) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V into certain number of strips, each of them corresponding to a hidden unit. We construct two neural networks based on these hidden units and show that they correctly compute the given but arbitrary multiple-valued function. Preliminary experimental results are presented and discussed. |
| doi_str_mv | 10.1109/72.914519 |
| format | article |
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A genetic algorithm (GA) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V into certain number of strips, each of them corresponding to a hidden unit. We construct two neural networks based on these hidden units and show that they correctly compute the given but arbitrary multiple-valued function. 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Preliminary experimental results are presented and discussed.</description><subject>Algebra</subject><subject>Algorithms</subject><subject>Computer science</subject><subject>Genetic algorithms</subject><subject>Hyperplanes</subject><subject>Learning</subject><subject>Logic functions</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Multi-layer neural network</subject><subject>Network synthesis</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Strip</subject><subject>Strips</subject><subject>Transfer functions</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqF0c1rFDEYBvAgim1XD149SPCg9JD65ntylGK1UFC03oQhmUm2UzOTbTJj6X9vll1a8GAhkEB-73N4H4ReUTihFMwHzU4MFZKaJ-iQGkEJgOFP6xuEJIYxfYCOSrkGqAjUc3RAGyYE1-YQ_fpx-f38GybY4jLnYUOcLb7Hk1-yjWTy823Kv_E6p9v5Ctu4TnmYr0YcUsbR2zwN0xqPS5yHTfTkj41LHQ7L1M1DmsoL9CzYWPzL_b1CP88-XZ5-IRdfP5-ffrwgnWiamTjlgAUpIei-Y5Q2ViinOYAC2fMgFJPedQ03vRahZ8qDDtoxaV0HMgTHV-j9LneT083iy9yOQ-l8jHbyaSltXY4SwI14VGrOheSq2cp3_5WskbpR1T8KlVGUclXh23_gdVryVBfTGgbbrHpW6HiHupxKyT60mzyMNt-1FNpt2a1m7a7sat_sAxc3-v5B7tut4PUODN77--_99F-pjqr2</recordid><startdate>20010301</startdate><enddate>20010301</enddate><creator>Ngom, A.</creator><creator>Stojmenovic, I.</creator><creator>Milutinovic, V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transaction on neural networks and learning systems, 2001-03, Vol.12 (2), p.212-227 |
| issn | 1045-9227 2162-237X 1941-0093 2162-2388 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28578673 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Algebra Algorithms Computer science Genetic algorithms Hyperplanes Learning Logic functions Mathematical analysis Mathematical models Multi-layer neural network Network synthesis Neural networks Neurons Strip Strips Transfer functions |
| title | STRIP - a strip-based neural-network growth algorithm for learning multiple-valued functions |
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