Equivalence in knowledge representation: Automata, recurrent neural networks, and dynamical fuzzy systems

C. Lee Giles, Christian W. Omlin, Karvel K. Thornber

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


Neurofuzzy systems - the combination of artificial neural networks with fuzzy logic - have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previously, it was proved that deterministic finite state automata (DFA) can be synthesized by or mapped into recurrent neural networks by directly programming the DFA structure into the weights of the neural network. Based on those results, a synthesis method is proposed for mapping fuzzy finite state automata (FFA) into recurrent neural networks. Furthermore, this mapping is suitable for direct implementation in very large scale integration (VLSI), i.e., the encoding of FFA as a generalization of the encoding of DFA in VLSI systems. The synthesis method requires FFA to undergo a transformation prior to being mapped into recurrent networks. The neurons are provided with an enriched functionality in order to accommodate a fuzzy representation of FFA states. This enriched neuron functionality also permits fuzzy parameters of FFA to be directly represented as parameters of the neural network. We also prove the stability of fuzzy finite state dynamics of the constructed neural networks for finite values of network weight and, through simulations, give empirical validation of the proofs. Hence, we prove various knowledge equivalence representations between neural and fuzzy systems and models of automata.

Original languageEnglish (US)
Pages (from-to)1623-1640
Number of pages18
JournalProceedings of the IEEE
Issue number9
StatePublished - 1999

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


Dive into the research topics of 'Equivalence in knowledge representation: Automata, recurrent neural networks, and dynamical fuzzy systems'. Together they form a unique fingerprint.

Cite this