Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Abstract

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.

Keywords:

• spiking neural P systems
• fuzzy spiking neural P systems
• adaptive fuzzy spiking neural P systems
• fuzzy reasoning
• learning problem

2010 AMS Subject Classifications:

• 03B52
• 47S40
• 92B20
• 94D05

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61170030), the Open Research Fund of Key Laboratory of Advanced Scientific Computation, Xihua University (No. S2jj2012-002) Research Fund of Sichuan Key Laboratory of Intelligent Network Information Processing (No. SGXZD1002-10), Importance Project Foundation of the Education Department of Sichuan province (No. 12ZA163), and the Importance Project Foundation of Xihua University (No. Z1122632), China.