Pruning of Rule Base of a Neural Fuzzy Inference Network

Smarti Reel, Ashok Kumar Goel

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this work, Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.

Original languageEnglish
Title of host publicationContemporary Computing
Subtitle of host publication 4th International Conference, IC3 2011, Noida, India, August 2011 Proceedings
Place of PublicationBerlin, Heidelberg
Number of pages2
ISBN (Electronic)9783642226069
ISBN (Print)9783642226052
Publication statusPublished - Aug 2011
Event4th International Conference on Contemporary Computing, IC3 2011 - Noida, India
Duration: 8 Aug 201110 Aug 2011

Publication series

NameCommunications in Computer and Information Science (CCIS)
ISSN (Print)1865-0929


Conference4th International Conference on Contemporary Computing, IC3 2011
Abbreviated titleIC3 2011


  • artificial intelligence
  • fuzzy logic
  • Neural fuzzy inference network
  • neural network
  • pruning

ASJC Scopus subject areas

  • Computer Science(all)


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