Unification neural networks: unification by error-correction learning

E. Komendantskaya

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    We show that the conventional first-order algorithm of unification can be simulated by finite artificial neural networks with one layer of neurons. In these unification neural networks, the unification algorithm is performed by error-correction learning. Each time-step of adaptation of the network corresponds to a single iteration of the unification algorithm. We present this result together with the library of learning functions and examples fully formalised in MATLAB Neural Network Toolbox. © The Author 2010. Published by Oxford University Press. All rights reserved.
    Original languageEnglish
    Pages (from-to)821-847
    Number of pages27
    JournalLogic Journal of the Interest Group in Pure and Applied Logic (IGPL) (Logic Journal of the IGPL)
    Issue number6
    Publication statusPublished - Dec 2011


    • Unification
    • Neuro-Symbolic Networks
    • Neural Network Learning
    • Error-correction Learning
    • Hybrid Networks
    • Connectionism


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