Integration of knowledge-based systems and neural networks: Neuro-expert Petri net models and applications

X. F. Zha, S. Y E Lim, S. C. Fok

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

There is now a growing realization in the intelligent systems community that many complex problems require hybrid solutions. This paper identifies and describes how knowledge based systems (KBSs), fuzzy logic (FL) and artificial neural networks (ANNs) can be integrated, and provides a novel (fuzzy) expert Petri net (EPN, FEPN) based approach for the integration of KBSs and ANNs. A generic expert Petri net model of single neuron is presented, and a two-layer generic Petri net model for (fuzzy) neural networks neural (fuzzy) expert Petri nets (NEPN, NFEPN) that use this neuron model as a building block is described. The NEPN and NFEPN models can be used for representing a (fuzzy) knowledge base and (fuzzy) reasoning. And also, they can be utilized to develop ANN-like multilayered Petri net architectures of distributed hybrid intelligence having learning ability. Some application examples are illustrated for validation of the proposed models.

Original languageEnglish
Pages (from-to)1423-1428
Number of pages6
JournalIEEE International Conference on Robotics & Automation 1998
Volume2
Publication statusPublished - 1998
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Integration of knowledge-based systems and neural networks: Neuro-expert Petri net models and applications'. Together they form a unique fingerprint.

Cite this