Organisms modeling: The question of radial basis function networks
There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently...
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Format: | Tagungsbericht |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF) networks) in the context of systems and biological reactive organisms. |
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ISSN: | 2271-2097 2431-7578 2271-2097 |
DOI: | 10.1051/itmconf/20140303002 |