An Adaptive Resource Allocating Neuro-Fuzzy Inference System with Sensitivity Analysis Resource Control

  • Minas Pertselakis
  • Natali Raouzaiou
  • Andreas Stafylopatis
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. Our approach involves a neurofuzzy model with structural learning capabilities that adds rule nodes when necessary during the training phase. Sensitivity analysis is then applied to the trained network so as to evaluate the network rules and control their usage in a dynamic manner based on a confidence threshold. Therefore, on one hand, we result in a well-balanced structure with an improved adaptive behavior and, on the other hand, we propose a way to control and restrict the “curse of dimensionality”. The experimental results on a number of classification problems prove clearly the strengths and benefits of this approach.


Hide Node Confidence Measure Rule Node Network Rule Neurofuzzy System 
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

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Minas Pertselakis
    • 1
  • Natali Raouzaiou
    • 1
  • Andreas Stafylopatis
    • 1
  1. 1.National Technical University of Athens School of Electrical and Computer EngineeringAthensGreece

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