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Journals / The Journal of Cognitive Systems / 2016 / Cilt: 1 - Sayı: 1

NOTE ON THE INFORMATION-THEORETIC ASPECT OF FUZZY NEURAL TREE

Ozer CİFTCİOGLU
Michael S. BİTTERMANN
Pages
37–40
Publication Date
2016-12-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The present note is to highlight this aspect of the tree as this is important in working of the tree with its knowledge-driven structure.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:A note on the information-theoretic aspect of the fuzzy neural tree (FNT) is presented.
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Key Findings & Data:The detailed description of the FNT is given in an earlier work, where its information-theoretic aspect is heuristically mentioned but not elaborated because of some space limitation.
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Conclusion & Impact:The present note is to highlight this aspect of the tree as this is important in working of the tree with its knowledge-driven structure.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Fuzzy logic · neural tree · knowledge modeling

Abstract

A note on the information-theoretic aspect of the fuzzy neural tree (FNT) is presented. The detailed description of the FNT is given in an earlier work, where its information-theoretic aspect is heuristically mentioned but not elaborated because of some space limitation. The present note is to highlight this aspect of the tree as this is important in working of the tree with its knowledge-driven structure.

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Cite This Article

CİFTCİOGLU, O., BİTTERMANN, M. S. (2016). NOTE ON THE INFORMATION-THEORETIC ASPECT OF FUZZY NEURAL TREE. The Journal of Cognitive Systems, Cilt: 1 - Sayı: 1, 37–40. https://ajindex.com/articles/806284

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References (8)

  1. Ö. Ciftcioglu and M. S. Bittermann, "A Fuzzy Neural Tree Based on Likelihood," presented at the 2015 IEEE International Conference on Fuzzy Systems - FUZZ-IEEE 2015, Istanbul, Turkey, 2015
  2. D. Dubois and H. Prade, "A semantics for possibility theory based on likelihoods," J. of Mathematical Analysis and Applications pp. 205, 359- 380, 1997
  3. D. Dubois and H. Prade, "A semantics for possibility theory based on likelihoods," in Int. Joint Conf. of Fourth IEEE Int. Conf. on Fuzzy Systems and Second Int. Fuzzy Engineering Symposium - Fuzzy Systems 1995, Yokohama, Japan, 1995, pp. 1597-1604
  4. A. Papoulis, Probability, Random Variables and Stochastic Processes. New York: McGraw-Hill, 1965
  5. Y. Pawitan, In All Likelihood: Statistical Modelling and Inference Using Likelihood. New York: Clarendon Press Oxford, 2001
  6. L. A. Zadeh, "Is there a need for fuzzy logic?," Information Sciences, vol. 178, pp. 2751-2779, 2008
  7. Ö. Ciftcioglu and M. S. Bittermann, "Computational Cognitive Color Perception," presented at the IEEE World Congress on Computational Intelligence - WCCI 2016, Vancouver, Canada, 2016
  8. O. Ciftcioglu and M. S. Bittermann, "Fuzzy Neural Tree in Evolutionary Computation for Architectural Design Cognition," in IEEE Congress on Evolutionary Computation - CEC 2015, Sendai, Japan, 2015, pp. 2319 -2326.