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GenSoFNN: a generic self-organizing fuzzy neural network.

W L Tung1, C Quek

  • 1Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary
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A novel generic self-organizing fuzzy neural network (GenSoFNN) overcomes limitations of existing neuro-fuzzy systems. It automatically generates consistent fuzzy rules from data, demonstrating strong noise tolerance and eliminating the need for prior knowledge.

Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Machine Learning

Background:

  • Existing neuro-fuzzy networks fall into two categories: self-tuning fuzzy systems requiring initial rules, and systems that automatically formulate rules from data via clustering.
  • However, most current neuro-fuzzy systems suffer from inconsistent rule bases, heuristic node operations, susceptibility to noisy data, and the need for prior knowledge like the number of clusters.

Purpose of the Study:

  • To propose a novel neural fuzzy system, the generic self-organizing fuzzy neural network (GenSoFNN), designed to overcome the deficiencies of existing approaches.
  • To enhance noise tolerance and ensure a consistent, compact rule base in neural fuzzy systems.

Main Methods:

  • Introduced the generic self-organizing fuzzy neural network (GenSoFNN).

Related Experiment Videos

  • Employed a novel clustering technique, discrete incremental clustering (DIC), for enhanced noise tolerance.
  • Incorporated built-in mechanisms within GenSoFNN to identify and prune redundant or obsolete fuzzy rules, ensuring rule-base consistency and compactness.

    • Main Results:

    • The proposed GenSoFNN demonstrated strong tolerance to noisy training data through the discrete incremental clustering (DIC) technique.
    • The GenSoFNN network successfully generated a consistent and compact fuzzy rule base by automatically identifying and removing redundant rules.
    • Performance evaluations through extensive simulations showed encouraging results for GenSoFNN when benchmarked against other neural and neuro-fuzzy systems.

    Conclusions:

    • The GenSoFNN presents a significant advancement in neural fuzzy systems, effectively addressing key limitations of prior work.
    • Its ability to handle noisy data and automatically generate a consistent rule base without prior knowledge makes it a robust and versatile tool.
    • The encouraging performance benchmarks suggest GenSoFNN's potential for broader application in intelligent systems development.