Classification with Spiking Neural P Systems
摘要
As a branch of the third generation of artificial neural networks in terms of computational units, spiking neural P systems (SNP systems) with learning ability for solving a variety of classification problems are a very promising and fascinating ongoing research direction in the community of membrane computing, machine learning, and artificial intelligence. This chapter presents the good potential and advantages of SNP systems in the area of pattern recognition. Two representative variants of SNP systems, layered SNP systems and learning numerical SNP systems, are introduced from the perspective of models, algorithms, and experiments. Simulation results are provided to show the feasibility, effectiveness, and merits.