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Pruning of Rule Base of a Neural Fuzzy Inference Network

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Contemporary Computing (IC3 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 168))

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Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Electronics and Communication Engineering Department, Thapar University, Patiala, 147001, India

    Smarti Reel

  2. M M Group of Institutions, V.P.O Sadopur, Distt., Ambala, 134003, India

    Ashok Kumar Goel

Authors
  1. Smarti Reel
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  2. Ashok Kumar Goel
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Editor information

Editors and Affiliations

  1. Iowa State University and IIT Bombay, India, 329 Durham, Ames, IA 5001, Iowa, USA

    Srinivas Aluru

  2. Indian Statistical Institute, 203 B.T. Road, 700 108, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. The Ohio State University, 3190 Graves Hall, 333 W 10th Ave, 43210, Columbus, OH, USA

    Umit V. Catalyurek

  4. Department of Computing Science, Chalmers University, Rännvagen 6B, 412 96, Göteborg, Sweden

    Devdatt P. Dubhashi

  5. Dept. of Electrical and Computer Engineering, Iowa State University, 329 Durham, IA 50011, Ames, USA

    Phillip H. Jones

  6. TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Brett Road, NJ 08854-8058, Piscataway, USA

    Manish Parashar

  7. School of Computer Engineering, Nanyang Technological University, N4-02a-32 Nanyang Ave, 639798, Singapore

    Bertil Schmidt

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© 2011 Springer-Verlag Berlin Heidelberg

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Reel, S., Goel, A.K. (2011). Pruning of Rule Base of a Neural Fuzzy Inference Network. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_55

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  • DOI: https://doi.org/10.1007/978-3-642-22606-9_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22605-2

  • Online ISBN: 978-3-642-22606-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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