期刊
IEEE TRANSACTIONS ON NANOBIOSCIENCE
卷 18, 期 2, 页码 176-190出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNB.2019.2896981
关键词
Bio-inspired computing; membrane computing; spiking neural P system; learning; letter classification
资金
- National Natural Science Foundation of China [61320106005, 61502535, 61772214, 61873280]
- Key Research and Development Program of Shandong Province [2017GGX10147]
- AEI/FEDER, Spain, EU [TIN2016-81079-R]
- Talento-Comunidad de Madrid [2016-T2/TIC-2024]
- MINECO AEI/FEDER, Spain, EU [TIN2016-81079-R]
- FSE/FEDER, Comunidad de Madrid, EU, through the InGEMICS-CM Project [B2017/BMD-3691]
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.
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