Skip to main content
SpringerLink
Log in

Framework of fully integrated hybrid systems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A framework of fully integrated hybrid systems (HSs) is proposed for the development and management of HS which involve databases, advanced user interfaces, symbolic systems, and artificial neural networks. This framework provides a common input–output interface among those HS modules developed on the framework, with a completely two-directional flow control and a highly parallel processing. This integration framework facilitates the incorporation of heterogeneous modules, together with their subsequent management and updating.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. McGarry K, Wermter S, MacIntyre J (1999) Hybrid neural systems: from simple coupling to fully integrated neural networks. Neural Comput Surv 2:62–94

    Google Scholar 

  2. Wermter S, Sun R (2000) Hybrid neural systems. Springer, New York

    Book  Google Scholar 

  3. Sun R (2002) Hybrid systems and connectionist implementationalism. In: Encyclopedia of cognitive science. Nature Publishing Group (MacMillan), London, pp 697–703

  4. Sun R (1994) Integrating rules and connectionism for robust commonsense reasoning. Wiley, New York

    MATH  Google Scholar 

  5. Corchado E, Corchado JM, Abraham A (2007) Innovations in hybrid intelligent systems. Springer, Heidelberg

    Book  MATH  Google Scholar 

  6. Christos C, Giua A, Seatzu C, Zaytoon J (2006) Analysis and design of hybrid systems. Elsevier, Alghero

    Google Scholar 

  7. Al-Zobaidie A, Grimson JB (1987) Expert systems and database systems: how can they serve each other? Expert Syst 4:30–37

    Article  Google Scholar 

  8. Codd EF (1972) Derivability, redundancy and consistency of relations in large data banks. IBM research report RJ 599

  9. Codd EF (1982) Relational database: a practical foundation for productivity. Commun ACM 25:109–117

    Article  Google Scholar 

  10. Santos A (1998) Methodology for hybrid systems including expert systems, artificial neural networks and databases. Ph.D. thesis. University of A Coruña, A Coruña

  11. Santos A, Arcay B, Dorado J, Pazos A (2002) Artificial neural networks manipulation server. Research on the integration of data bases and artificial neural networks. Neural Comput & Appl 11:3–16

    Article  MATH  Google Scholar 

  12. Weiss SM, Kulikowski CA (1991) How to estimate the true performance of a learning system. In: Weiss SM, Kulikowski CA (eds) Computer systems that learn. Morgan Kaufmann, San Francisco, pp 17–49

    Google Scholar 

  13. Walker LJ, Johnston J (1999) Guidelines for the assessment of indirect and cumulative impacts as well as impact interactions. Office for official publications of the European Communities, Luxembourg

    Google Scholar 

  14. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Wesley, Massachusets

    MATH  Google Scholar 

  15. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan

    Google Scholar 

  16. Holland JH (1996) Complex adaptive systems. In: Pazos A (ed) Artificial neural networks and adaptive systems. University of A Coruña Press, A Coruña, pp 259–295

    Google Scholar 

  17. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

  18. Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, New Jersey

    Google Scholar 

  19. Dasgupta D (1999) Artificial immune systems and their applications. Springer, Berlin

    Book  MATH  Google Scholar 

  20. Feldman JA, Ballard DH (1982) Connectionist models and their properties. Cogn Sci 6(1):205–254

    Google Scholar 

  21. Tickle A, Maire F, Bologna G, Andrews R, Diederich J (2000) Lessons from past, current issues and future research directions in extracting the knowledge embedded in artificial neural networks. In: Wermter S, Sun R (eds) Hybrid neural systems. Springer, Heidelberg, pp 230–245

    Google Scholar 

  22. Teodorescu H (2000) Intelligent systems and interfaces. Kluwer, Boston

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Communications and Information Technologies, School of Computer Science, A Coruña University, 15071, A Coruña, Spain

    A. Santos, J. J. Romero, A. Carballal & A. Pazos

Authors
  1. A. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. J. Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Carballal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Pazos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Santos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Santos, A., Romero, J.J., Carballal, A. et al. Framework of fully integrated hybrid systems. Neural Comput & Applic 21, 45–53 (2012). https://doi.org/10.1007/s00521-011-0672-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0672-9

Keywords

Search

Navigation