Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Interactive Visualization of Big Data Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Improvement of Precision of Neuro-Fuzzy System by Increase of Activation of Rules

Author : Krzysztof Siminski

Published in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Neuro-fuzzy systems have proved to be a powerful tool for data approximation and generalization. A rule base is a crucial part of a neuro-fuzzy system. The data items activate the rules and their answers are aggregated into a final answer. The experiments reveal that sometimes the activation of all rules in a rule base is very low. It means the system recognizes the data items very poorly. The paper presents a modification of the neuro-fuzzy system: the tuning procedure has two objectives: minimizing of the error of the system and maximizing of the activation of rules. The higher activation (better recognition of the data items) makes the model more reliable. The increase of the activation of rules may also decrease the error rate for the model. The paper is accompanied by the numerical examples.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter New Rough-Neuro-Fuzzy Approach for Regression Task in Incomplete Data
next chapter Rough Sets in Multicriteria Classification of National Heritage Monuments
Literature
1.
Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17(2–3), 255–287 (2011)
2.
Bezerra, R.A., Vellasco, M.M., Tanscheit, R.: Hierarchical neuro-fuzzy BSP Mamdani system. In: Neural Networks, Genetic Algorithms and Soft Computing, pp. 1321–1326 (2005)
3.
Czabański, R.: Extraction of fuzzy rules using deterministic annealing integrated with \(\epsilon \)-insensitive learning. Int. J. Appl. Math. Comput. Sci. 16(3), 357–372 (2006)MathSciNetMATH
4.
Czogała, E., Łęski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Studies in Fuzziness and Soft Computing. Physica-Verlag, Heidelberg (2000)CrossRefMATH
5.
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. J. Cybern. 3(3), 32–57 (1973)MathSciNetCrossRefMATH
6.
Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010)
7.
Jakubek, S., Keuth, N.: A local neuro-fuzzy network for high-dimensional models and optimalization. Eng. Appl. Artif. Intell. 19(6), 705–717 (2006)CrossRef
8.
Łęski, J.: \(\varepsilon \)-insensitive learing techniques for approximate reasoning systems. Int. J. Comput. Cogn. 1(1), 21–77 (2003)
9.
Łęski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets Syst. 108(3), 289–297 (1999)MathSciNetCrossRefMATH
10.
Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. C–26(12), 1182–1191 (1977)CrossRefMATH
11.
Senhadji, R., Sanchez-Solano, S., Barriga, A., Baturone, I., Moreno-Velo, F.: Norfrea: an algorithm for non redundant fuzzy rule extraction. IEEE Int. Conf. Syst. Man Cybern. 1, 604–608 (2002)CrossRef
12.
Setnes, M., Babuška, R.: Rule base reduction: some comments on the use of orthogonal transforms. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 31(2), 199–206 (2001)CrossRef
13.
Sikora, M., Krzystanek, Z., Bojko, B., Śpiechowicz, K.: Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings. J. Min. Sci. 47(4), 493–505 (2011)CrossRef
14.
Simiński, K.: Patchwork neuro-fuzzy system with hierarchical domain partition. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 11–18. Springer, Heidelberg (2009)CrossRef
15.
Simiński, K.: Neuro-fuzzy system based kernel for classification with support vector machines. In: Gruca, A., Czachórski, T., Kozielski, S. (eds.) Man-Machine Interactions 3. AISC, vol. 242, pp. 415–422. Springer, Heidelberg (2014)CrossRef
16.
17.
Siminski, K.: Ridders algorithm in approximate inversion of fuzzy model with parameterized consequences. Expert Syst. Appl. 51, 276–285 (2016)CrossRef
18.
Sugeno, M., Tanaka, K.: Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets Syst. 42(3), 315–334 (1991)MathSciNetCrossRefMATH
19.
Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1(1), 7–31 (1993)CrossRef
20.
Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)CrossRef
21.
Zhou, Z.H., Chen, S.F.: Rule extraction from neural networks. J. Comput. Res. Dev. 39(4), 398–405 (2002)
Metadata
Title
Improvement of Precision of Neuro-Fuzzy System by Increase of Activation of Rules
Author
Krzysztof Siminski
Copyright Year
2016
Book
Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-34099-9_11

Premium Partner

    Image Credits