Tunability of auto resonance network
| Autor: | V. M. Aparanji, R. Aparna, Uday Wali |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Polynomial
Artificial neural network Computer science business.industry General Chemical Engineering Deep learning General Engineering General Physics and Astronomy Resonance Topology General Earth and Planetary Sciences General Materials Science Artificial intelligence business Computer Science::Databases General Environmental Science Degradation (telecommunications) |
| Zdroj: | SN Applied Sciences. 2 |
| ISSN: | 2523-3971 2523-3963 |
| DOI: | 10.1007/s42452-020-2737-9 |
| Popis: | This paper proposes a new type of Artificial Neural Network called Auto-Resonance Network (ARN) derived from synergistic control of biological joints. The network can be tuned to any real valued input without any degradation of learning rate. Neuronal density of the network is low and grows at a linear or low order polynomial rate with input classification. Input coverage of the neuron can be tuned dynamically to match properties of input data. ARN can be used as a part of hierarchical structures to support deep learning applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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