Skip to main content
SpringerLink
Log in

A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

Included in the following conference series:

Abstract

In this paper we propose a new method for evolutionary selection of parameters and structure of neuro-fuzzy system for nonlinear modelling. This method allows maintain the correct proportions between accuracy, complexity and interpretability of the system. Our algorithm has been tested using well-known benchmarks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aziz, D., Ali, M.A.M., Gan, K.B., Saiboon, I.: Initialization of Adaptive Neuro-Fuzzy Inference System Using Fuzzy Clustering in Predicting Primary Triage Category. In: 2012 4th International Conference on Intelligent and Advanced Systems, ICIAS, vol. 1, pp. 170–174 (2012)

    Google Scholar 

  2. Bartczuk, Ł., Rutkowska, D.: A new version of the fuzzy-ID3 algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1060–1070. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Bartczuk, Ł., Rutkowska, D.: Medical diagnosis with type-2 fuzzy decision trees. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds.) Computers in Medical Activity. AISC, vol. 65, pp. 11–21. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bartczuk, Ł., Rutkowska, D.: Type-2 fuzzy decision trees. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 197–206. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Bentley, P.: Evolutionary Design by Computers. Morgan Kaufmann (1999)

    Google Scholar 

  6. Box, G.E.P., Jenkins, G.M.: Time Series Analysis. In: Forecasting and Control, pp. 532–533 (1976)

    Google Scholar 

  7. Carlos, A.C.C., Gary, B.L., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer-Verlag, New York, Inc. (2007)

    Google Scholar 

  8. Casillas, J., Cordon, O., Herrera, F., Magdalena, L. (eds.): Interpretability Issues in Fuzzy Modeling. STUDFUZZ, vol. 128. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  9. Cierniak, R.: A new approach to image reconstruction from projections problem using a recurrent neural network. Applied Mathematics and Computer Science 18(2), 147–157 (2008)

    MathSciNet  Google Scholar 

  10. Cierniak, R.: A novel approach to image reconstruction problem from fan-beam projections using recurrent neural network. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 752–761. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Cierniak, R.: An image compression algorithm based on neural networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 706–711. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Cierniak, R.: New neural network algorithm for image reconstruction from fan-beam projections. Elsevier Science: Neurocomputing 72, 3238–3244 (2009)

    Article  Google Scholar 

  13. Cordon, O., Herrera, F., Hoffman, F., Magdalena, L.: Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. Word Scientific (2001)

    Google Scholar 

  14. Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno Neuro-Fuzzy Structures for Nonlinear Approximation. WSEAS Transactions on Systems 4(9), 1450–1458 (2005)

    Google Scholar 

  15. Cpałka, K.: A New Method for Design and Reduction of Neuro-Fuzzy Classification Systems. IEEE Transactions on Neural Networks 20(4), 701–714 (2009)

    Article  Google Scholar 

  16. Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis Series A: Theory, Methods and Applications 71(12), e1659–e1672 (2009)

    Google Scholar 

  17. Cpałka, K., Rutkowski, L.: A new method for designing and reduction of neuro-fuzzy systems. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 1851–1857 (2006)

    Google Scholar 

  18. Delgado, M., Gómez-Skarmeta, A.F., Martin, F.: Fuzzy clustering-based rapid prototyping for fuzzy rule-based modelling. IEEE Transaction on Fuzzy Systems 5, 223–233 (1997)

    Article  Google Scholar 

  19. Diago, L., Kitaoka, T., Hagiwara, I., Kambayashi, T.: Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set. IEEE Transactions on Neural Networks, 2422–2234 (2011)

    Google Scholar 

  20. Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 3rd edn. IEEE Press, Piscataway (2006)

    Google Scholar 

  21. Freitas, A.: Data Mining and Knowledge Discovery With Evolutionary Algorithms. Springer (2002)

    Google Scholar 

  22. Gabryel, M., Cpałka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: I Workshop on Genetic Fuzzy Systems, Genewa, pp. 119–123 (2005)

    Google Scholar 

  23. Gabryel, M., Rutkowski, L.: Evolutionary Learning of Mamdani-Type Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 354–359. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  24. Gabryel, M., Rutkowski, L.: Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 398–404. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Gan, L., Laurence, A., Maguib Raouf, N.G., Dadios Elmer, P., Avila Jose Maria, C.: Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images. In: TENCON 2012 - 2012 IEEE Region 10 Conference, pp. 1–5 (2012)

    Google Scholar 

  26. Hisao, I., Yusuke, N.: Discussions on Interpretability of Fuzzy Systems using Simple Examples. In: European Society for Fuzzy Logic and Technology - EUSFLAT, pp. 1649–1654

    Google Scholar 

  27. Horzyk, A., Tadeusiewicz, R.: Self-Optimizing Neural Networks. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 150–155. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  28. Kaur, G.: Similarity measure of different types of fuzzy sets. School of Mathematics and Computer Applications, Tharpar University (2010)

    Google Scholar 

  29. Kim, E., Park, M., Kimand, S.: A transformed input-domain approach to fuzzy modelling. IEEE Transaction on Fuzzy Systems 6, 596–604 (1998)

    Article  Google Scholar 

  30. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers (2000)

    Google Scholar 

  31. Korytkowski, M., Gabryel, M., Rutkowski, L., Drozda, S.: Evolutionary methods to create interpretable modular system. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 405–413. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  32. Korytkowski, M., Rutkowski, L., Scherer, R.: On combining backpropagation with boosting. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN, Vancouver, pp. 1274–1277 (2005)

    Google Scholar 

  33. Krishnaji, A., Rao, A.A.: Implementation of a hybrid Neuro Fuzzy Genetic System for improving protein secondary structure prediction. In: 2012 National Computing and Communication Systems (NCCCS), pp. 1–5 (2012)

    Google Scholar 

  34. Laskowski, Ł.: A novel hybrid-maximum neural network in stereo-matching process. Neural Computing & Applications (2012), doi:10.1007/s00521-012-1202-0

    Google Scholar 

  35. Laskowski, Ł.: Objects auto-selection from stereo-images realised by self-correcting neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS (LNAI), vol. 7267, pp. 119–125. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  36. Laskowski, Ł.: A novel continuous dual mode neural network in stereo-matching process. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS (LNAI), vol. 6354, pp. 294–297. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  37. Lin, J., Zheng, Y.B.: Vibration control of rotating plate by decomposed neuro-fuzzy control with genetic algorithm tuning. In: 2012 IEEE International Conference on Control Applications, CCA, pp. 575–580 (2012)

    Google Scholar 

  38. Li, X., Er, M.J., Lim, B.S., et al.: Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection. International Journal of Neural Systems 20(5), 405–419 (2010)

    Article  Google Scholar 

  39. Lin, Y., Cunningham III, G.A.: A New Approach To Fuzzy-Neural System Modeling. IEEE Transactions on Fuzzy Systems 3, 190–198 (1995)

    Article  Google Scholar 

  40. Michalewicz, Z.: Genetic Algorithms + Data Structures=Evolution Programs. Springer (1999)

    Google Scholar 

  41. Nowicki, R., Pokropińska, A.: Information criterions applied to neuro-fuzzy architectures design. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 332–337. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  42. Nowicki, R., Scherer, R., Rutkowski, L.: A method for learning of hierarchical fuzzy systems. In: Sincak, P., Vascak, J., Kvasnicka, V., Pospichal, J. (eds.) Intelligent Technologies - Theory and Applications, pp. 124–129. IOS Press (2002)

    Google Scholar 

  43. Pal, N.R., Chakraborty, D.: Simultaneous Feature Analysis and SI. In: Neuro-Fuzzy Pattern Recognition. World Scientific, Singapore (2000)

    Google Scholar 

  44. Przybył, A.: Doctoral dissertation: Adaptive observer of induction motor using artificial neural networks and evolutionary algorithms. Poznan University of Technology (2003) (in Polish)

    Google Scholar 

  45. Przybył, A., Smoląg, J., Kimla, P.: Real-time Ethernet based, distributed control system for the CNC machine. Electrical Review 2010-2 (2010) (in Polish)

    Google Scholar 

  46. Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 697–705. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  47. Rutkowska, D., Nowicki, R., Rutkowski, L.: Neuro-fuzzy architectures with various implication operators. In: Sincak, P., et al. (eds.) The State of the Art in Computational Intelligence, pp. 214–219 (2000)

    Google Scholar 

  48. Rutkowski, L.: Computational Intelligence. Springer (2007)

    Google Scholar 

  49. Rutkowski, L.: Flexible Neuro-Fuzzy Systems. Kluwer Academic Publishers (2004)

    Google Scholar 

  50. Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Trans. Neural Networks 14(3), 554–574 (2003)

    Article  Google Scholar 

  51. Rutkowski, L., Cpałka, K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th Neural Information Processing, pp. 1857–1861 (2002)

    Google Scholar 

  52. Rutkowski, L., Przybył, A., Cpałka, K.: Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation. IEEE Transactions on Industrial Electronics 59(2), 1238–1247 (2012)

    Article  Google Scholar 

  53. Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online Speed Profile Generation for Industrial Machine Tool Based on Neuro Fuzzy Approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  54. Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: 6th International Conference on Neural Networks and Soft Computing, Zakopane, Poland. Advances In Soft Computing, pp. 563–569 (2003)

    Google Scholar 

  55. Scherer, R., Rutkowski, L.: Connectionist fuzzy relational systems. In: Halgamuge, S.K., Wang, L. (eds.) Computational Intelligence for Modelling and Prediction. SCI, vol. 2, pp. 35–47. Springer, Heidelberg (2005)

    Google Scholar 

  56. Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  57. Szaleniec, M., Goclon, J., Witko, M., Tadeusiewicz, R.: Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase. Journal of Computer-Aided Molecular Design 20(3), 145–157 (2006)

    Article  Google Scholar 

  58. Rey, M.I., Galende, M., Sainz, G.I., Fuente, M.J.: Checking orthogonal transformations and genetic algorithms for selection of fuzzy rules based on interpretability-accuracy concepts. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1271–1278 (2011)

    Google Scholar 

  59. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms, pp. I-XIX, 1-442. Springer (2008)

    Google Scholar 

  60. Subramanian, K., Suresh, S., Venkatesh Babu, R.: Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2012)

    Google Scholar 

  61. Sugeno, M., Tanaka, K.: Successive identification on a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems 42, 315–334 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  62. Sugeno, M., Yasakuwa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems, 7–31 (1993)

    Google Scholar 

  63. Wang, N., Hu, C., Shi, W.: A Mamdani Fuzzy Modeling Method via Evolution-Objective Cluster Analysis. In: 2012 31st Chinese Control Conference (CCC), pp. 3470–3475 (2012)

    Google Scholar 

  64. Wang, L.X., Langari, R.: Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques. IEEE Transaction on Fuzzy Systems 3, 454–458 (1995)

    Article  Google Scholar 

  65. Yong, L., Singh, C.: Evaluation of the failure rates of transmission lines during hurricanes using a neuro-fuzzy system. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 569–574 (2010)

    Google Scholar 

  66. Yoshinari, Y., Pedrycz, W., Hirota, K.: Construction of fuzzy models through clustering techniques. Fuzzy Sets and Systems 54, 157–165 (1993)

    Article  MathSciNet  Google Scholar 

  67. Zitzler, E., Laumanns, M., Bleuler, S.: A Tutorial on Evolutionary Multiobjective Optimization. In: Metaheuristics for Multiobjective Optimisation, pp. 3–38 (2003)

    Google Scholar 

  68. Zalasiński, M., Cpałka, K.: A new method of on-line signature verification using a flexible fuzzy one-class classifier. Selected Topics in Computer Science Applications, pp. 38–53. EXIT (2011)

    Google Scholar 

  69. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS (LNAI), vol. 7268, pp. 362–367. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czȩstochowa University of Technology, Poland

    Krystian Łapa, Marcin Zalasiński & Krzysztof Cpałka

Authors
  1. Krystian Łapa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcin Zalasiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krzysztof Cpałka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Łapa, K., Zalasiński, M., Cpałka, K. (2013). A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38658-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation