Spiking Neural P Systems With Learning Functions
Spiking Neural P Systems With Learning Functions
复制标题
具有学习功能的尖峰神经 P 系统
DOI：
10.1109/tnb.2019.2896981
复制
发表时间：
2019-04-01
影响因子：
3.9
通讯作者：
Rodriguez-Paton, Alfonso
中科院分区：
文献类型：
--
作者：
Song, Tao;Pan, Linqiang;Rodriguez-Paton, Alfonso
脉冲神经P系统（Spiking neural P systems，SN P systems）是一类分布式并行的类神经计算模型，其灵感来自于神经元通过脉冲进行通信的方式。本文介绍了一种新的系统变形，称为具有学习功能的SN P系统。这样的系统可以在计算过程中动态地加强和削弱神经元之间的连接。构造了一类具有简单Hebbian学习函数的特殊SN-P系统，用于识别英文字母.实验结果表明，在无噪声的测试用例中，SNP系统的平均准确率达到98.76%。在低、中、高噪声的测试情况下，SNP系统的性能优于BP神经网络和概率神经网络。此外，与脉冲神经网络相比，SN P系统在识别带噪声的字母时性能稍好。本文的结果是有希望的，这是第一次尝试使用SN P系统在模式识别后，SN P系统的许多理论的进步，和SN P系统解决模式识别问题的可行性。
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.