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

Conference paper

DOI: 10.1007/978-3-319-34099-9_11

Part of the Communications in Computer and Information Science book series (CCIS, volume 613)
Cite this paper as:
Siminski K. (2016) Improvement of Precision of Neuro-Fuzzy System by Increase of Activation of Rules. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS 2015, BDAS 2016. Communications in Computer and Information Science, vol 613. Springer, Cham

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.

Keywords

Neuro-fuzzy system Activation of rules 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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