Fuzzy Parameters and Cutting Forces Optimization via Genetic Algorithm Approach

  • Stefania GallovaEmail author
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 60)


The classification of solved signal features for manufacturing process condition monitoring has been carried out using fuzzy parameters optimization processing. In cases where assumptions in respect of nonlinear behavior cannot be made, the need to describe mathematically, ever increasing complexity become difficult and perhaps infeasible. The optimization possibilities of the fuzzy system parameters using genetic algorithms are studied. An analytical function determines the positions of the output fuzzy sets in each mapping process, that substitute the fuzzy rule base used in conventional approach. We realize case adaptation by adjusting the fuzzy sets parameters. Fuzzy parameters within optimization procedure could be multiobjective. We solve also the system for cutting process simulation, which contains the experimental model and the simulation model based on genetic algorithms. There is developed a genetic algorithm based simulation procedure for the prediction of the cutting forces. These genetic algorithms methodologies are suitable for fuzzy implementation control and for solution of large-scale problems.


Fuzzy parameter optimization cutting forces optimization genetic algorithm fitness 


  1. 1.
    Herrera-Viedma, E.: Modelling the retrieval process of an information retrieval system using an ordinal linguistic approach. Am. Soci. Inf. Sc. 6, 460–475 (2001)CrossRefGoogle Scholar
  2. 2.
    Gallova, S.: Fault diagnosis of manufacturing processes via genetic algorithm approach. IAENG Eng. Lett. 15(2), 349–355 (2007)Google Scholar
  3. 3.
    Rochio, I.J.: Relevance Feedback of Information Retrieval, The Smart System Experiments in Automatic Document of Processing, pp. 313–323. Prentice-Hall, New York (1971)Google Scholar
  4. 4.
    Gallova, S.: A maximum entropy inference within uncertain information reasoning. Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 1803–1810, Les Cordeliers, Paris (2006)Google Scholar
  5. 5.
    Ballé, P.: Fuzzy model-based parity equations for fault isolation. Con. Eng. Prac. 7(2), 261–270 (1999)CrossRefGoogle Scholar
  6. 6.
    Brini, A.: Introduction de la Gradulaite dans le Jugement Utilisateur, Dea Report, Toulouse, France (2002)Google Scholar
  7. 7.
    Pasi, G.: A logical formulation of the boolean model and weighted boolean models. Lumis’99, University College London, England (1999)Google Scholar
  8. 8.
    Zadeh, L.: The Concept of Linguistic Variable and It’s Application to Approximate Decision Making. Moscow, Mir (1976)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization And Machine Learning. Addison-Wesley, Reading, MA (1989)zbMATHGoogle Scholar
  10. 10.
    Lu, M., Dong, F., Fotouhi, F.: The semantic web, opportunities and challenges for next generation web applications. Inform. Res. 7(4) (2002)Google Scholar
  11. 11.
    Kruschwitz, U.: An adaptable search system for collections of partially structured documents. IEEE Intell. Syst. 18:44–52 (2003)CrossRefGoogle Scholar
  12. 12.
    Novakovic, B.: Fuzzy logic control synthesis without any rule base. IEEE Trans. Syst. Man Cyber 29(3):459–466 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Pavol Jozef Safarik University in KosiceKosiceSlovak Republic

Personalised recommendations