Skip to main content

Advertisement

SpringerLink
Log in

Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

An Erratum to this article was published on 06 November 2009

Abstract

One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with the conclusions. However, this way to work sometimes fails to obtain an optimal behaviour. To solve this problem, within the framework of Machine Learning, some Artificial Intelligence techniques could be successfully applied to enhance the controller behaviour.

Rule selection methods directly obtain a subset of rules from a given fuzzy rule set, removing inefficient and redundant rules and, thereby, enhancing the controller interpretability, robustness, flexibility and control capability. Besides, different parameter optimization techniques could be applied to improve the system accuracy by inducing a better cooperation among the rules composing the final rule base.

This work presents a study of how two new tuning approaches can be applied to improve FLCs obtained from the expert’s experience in non trivial problems. Additionally, we analyze the positive synergy between rule selection and tuning techniques as a way to enhance the capability of these methods to obtain more accurate and compact FLCs. Finally, in order to show the good performance of these approaches, we solve a real-world problem for the control of a heating, ventilating and air conditioning system.

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

Similar content being viewed by others

References

  1. Alcalá R, Benítez JM, Casillas J, Cordón O, Pérez R (2003) Fuzzy control of HVAC systems optimized by genetic algorithms. Appl Intell 18:155–177

    Article  MATH  Google Scholar 

  2. Alcalá R, Casillas J, Cordón O, González A, Herrera F (2005) A genetic rule weighting and selection process for fuzzy control of HVAC systems. Eng Appl Artif Intell 18(3):279–296

    Article  Google Scholar 

  3. Alcalá R, Alcalá-Fdez J, Casillas J, Cordón O, Herrera F (2006) Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling. Soft Comput 10(9):717–734

    Article  Google Scholar 

  4. Alcalá R, Alcalá-Fdez J, Herrera F (2007) A Proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635

    Article  Google Scholar 

  5. Alcalá R, Alcalá-Fdez J, Gacto MJ, Herrera F (2007) Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation. Soft Comput 11(5):401–419

    Article  Google Scholar 

  6. Alcalá R, Alcalá-Fdez J, Herrera F, Otero J (2007) Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation. International J Approx Reason 44(1):45–64

    Article  MATH  Google Scholar 

  7. Babuška R, Oosterhoff J, Oudshoorn A, Bruijn PM (2002) Fuzzy self-tuning PI control of pH in fermentation. Eng Appl Artif Intell 15(1):3–15

    Article  Google Scholar 

  8. Calvino F, Gennusa ML, Rizzo G, Scaccianoce G (2004) The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller. Energy Build 36:97–102

    Article  Google Scholar 

  9. Casillas J, Cordón O, Del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 13(1):13–29

    Article  Google Scholar 

  10. Cordón O, Herrera F (2000) A proposal for improving the accuracy of linguistic modeling. IEEE Trans Fuzzy Syst 8(3):335–344

    Article  Google Scholar 

  11. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore

    MATH  Google Scholar 

  12. Driankov D, Hellendoorn H, Reinfrank M (1993) An introduction to fuzzy control. Springer, Berlin

    MATH  Google Scholar 

  13. Eshelman LJ (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Foundations of Genetic Algorithms, vol 1. Morgan Kaufman, San Mateo, pp 265–283

    Google Scholar 

  14. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. Found Genet Algorithms 2:187–202

    Google Scholar 

  15. Gómez-Skarmeta AF, Jiménez F (1999) Fuzzy modeling with hybrid systems. Fuzzy Sets Syst 104:199–208

    Article  Google Scholar 

  16. Herrera F, Martńez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6):746–752

    Article  Google Scholar 

  17. Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy logic controllers by genetic algorithms. Int J Approx Reason 12:299–315

    Article  MATH  MathSciNet  Google Scholar 

  18. Herrera F, Lozano M, Verdegay JL (1997) Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst 92(1):21–30

    Article  Google Scholar 

  19. Hong TP, Lee CY (1999) Effect of merging order on performance of fuzzy induction. Intell Data Anal 3(2):139–151

    Article  MATH  Google Scholar 

  20. Huang S, Nelson RM (1994) Rule development and adjustment strategies of a fuzzy logic controller for an HVAC system—parts I and II (Analysis and Experiment). ASHRAE Trans 100(1):841–856

    Google Scholar 

  21. Ishibuchi H, Murata T, Türksen IB (1997) Single-objective and two objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 89(2):135–150

    Article  Google Scholar 

  22. Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H (1995) Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans Fuzzy Syst 3(3):260–270

    Article  Google Scholar 

  23. Jang JSR (1993) ANFIS: Adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–684

    Article  MathSciNet  Google Scholar 

  24. Karr C (1991) Genetic algorithms for fuzzy controllers. AI Expert 6(2):26–33

    Google Scholar 

  25. Krone A, Krause H, Slawinski T (2000) A new rule reduction method for finding interpretable and small rule bases in high dimensional search spaces. Proc IEEE Int Conf Fuzzy Syst 2:693–699

    Google Scholar 

  26. Krone A, Taeger H (2001) Data-based fuzzy rule test for fuzzy modelling. Fuzzy Sets Syst 123(3):343–358

    Article  MATH  MathSciNet  Google Scholar 

  27. Klose A, Nurnberger A, Nauck D (1998) Some approaches to improve the interpretability of neuro-fuzzy classifiers. In: Proceedings of the 6th European congress on intelligent techniques and soft computing, pp 629–633

  28. Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 121(12):1585–1588

    Google Scholar 

  29. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  30. Setnes M, Babuška R, Kaymak U, van Nauta Lemke HR (1998) Similarity measures in fuzzy rule base simplification. IEEE Trans Syst Man Cybern B: Cybern 28(3):376–386

    Article  Google Scholar 

  31. Setnes M, Roubos JA (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522

    Article  Google Scholar 

  32. Roubos JA, Setnes M (2001) Compact and transparent fuzzy models and classifiers through iterative complexity reduction. IEEE Trans Fuzzy Syst 9(4):516–524

    Article  Google Scholar 

  33. Yam Y, Baranyi P, Yang CT (1999) Reduction of fuzzy rule base via singular value decomposition. IEEE Trans Fuzzy Syst 7:120–132

    Article  Google Scholar 

  34. Yen J, Wang L (1999) Simplifying fuzzy rule-based models using orthogonal transformation methods. IEEE Trans Syst Man Cybern B: Cybern 29:13–24

    Article  Google Scholar 

  35. Whitley D, Kauth J (1988) GENITOR: A different genetic algorithm. In: Proceedings of the Rocky Mountain conference on artificial intelligence, pp 118–130

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Rafael Alcalá, Jesús Alcalá-Fdez, María José Gacto & Francisco Herrera

Authors
  1. Rafael Alcalá
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesús Alcalá-Fdez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María José Gacto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Alcalá.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s10489-009-0198-3

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alcalá, R., Alcalá-Fdez, J., Gacto, M.J. et al. Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems. Appl Intell 31, 15–30 (2009). https://doi.org/10.1007/s10489-007-0107-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-007-0107-6

Keywords

Search

Navigation