Boosting Ensemble of Relational Neuro-fuzzy Systems

  • Rafał Scherer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


In the paper a boosting ensemble of neuro-fuzzy relational systems is created. Rules in relational fuzzy systems are more flexible than rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights come from an additional binary relation. Thanks to this, input and output fuzzy sets are related to each other with a certain degree. The size of the relations is determined by the number of input fuzzy sets and the number of output fuzzy sets. Simulations performed on popular benchmarks show that the proposed ensemble outperforms other learning systems.


Fuzzy System Fuzzy Rule AdaBoost Algorithm Rule Weight Rule Consequents 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rafał Scherer
    • 1
    • 2
  1. 1.Department of Computer EngineeringCzęstochowa University of TechnologyCzęstochowaPoland
  2. 2.Department of Artificial IntelligenceWSHE University in ŁódźŁódźPoland

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