Abstract
This paper proposes Fractional-order Hopfield Neural Networks (FHNN). This network is mainly based on the classic well-known Hopfield net in which fractance components with fractional order derivatives, replace capacitors. Stability of FHNN is fully investigated through energy-like function analysis. To show how effective the FHNN network is, an illustrative example for parameter estimation problem of the second-order system is finally considered in the paper. The results of simulation are very promising.
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Boroomand, A., Menhaj, M.B. (2009). Fractional-Order Hopfield Neural Networks. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_108
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DOI: https://doi.org/10.1007/978-3-642-02490-0_108
Publisher Name: Springer, Berlin, Heidelberg
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