Fractional-order hopfield neural networks

142Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.
Get full text

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. © 2009 Springer Berlin Heidelberg.

Cite

CITATION STYLE

APA

Boroomand, A., & Menhaj, M. B. (2009). Fractional-order hopfield neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5506 LNCS, pp. 883–890). https://doi.org/10.1007/978-3-642-02490-0_108

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free