Spiking Neural P Systems With Learning Functions
| Autor: | Pan Zheng, Linqiang Pan, M. L. Dennis Wong, Tao Song, Tingfang Wu, Alfonso Rodríguez-Patón |
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| Rok vydání: | 2019 |
| Předmět: |
Spiking neural network
Artificial neural network Computer science business.industry Biomedical Engineering Probabilistic logic Pharmaceutical Science Medicine (miscellaneous) Bioengineering 02 engineering and technology 021001 nanoscience & nanotechnology Computer Science Applications Machine Learning Noise Hebbian theory Pattern recognition (psychology) Neural Networks Computer Artificial intelligence Electrical and Electronic Engineering Bio-inspired computing 0210 nano-technology business Membrane computing Biotechnology |
| Zdroj: | IEEE Transactions on NanoBioscience. 18:176-190 |
| ISSN: | 1558-2639 1536-1241 |
| DOI: | 10.1109/tnb.2019.2896981 |
| Popis: | Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems. |
| Databáze: | OpenAIRE |
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