Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Pattern Recognition

  • Cui Lin
  • Jun Li
  • Natasha Barrett
  • Yan-Qing Zhang
  • David A. Washburn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4845)


With ever-improving information technologies and high performance computational power, recent techniques in granular computing, soft computing and cognitive science have allowed an increasing understanding of normal and abnormal brain functions, especially in the research of human’s pattern recognition by means of computational intelligence. It is well understood that normal brains have high intelligence to recognize different geometrical patterns, but a systematic framework of biological neural network has not yet be established. In this paper, we propose the genetic granular cognitive fuzzy neural networks (GGCFNN) in order to efficiently testify artificial neural networks’ learning capability on human’s pattern recognition in term of symmetric and similar geometry patterns. In contrast to other information systems, the GGCFNN is a highly hybrid intelligent system integrating the techniques of genetic algorithms, granular computing, and fuzzy neural networks with cognitive science for pattern recognition. Our ability to simulate biological neural networks makes it possible a more comprehensive quantitative analysis on the pattern recognition of human brains, and our preliminary experiment results would shed lights on the future research of cognitive science and brain informatics.


Cognitive Science Fuzzy Neural Network Granular Computing Biological Neural Network Hybrid Neural Network 
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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  1. 1.
    Chen, H.P., Parng, T.M.: A new approach of multi-stage fuzzy logic inference. Fuzzy Sets and Systems 78, 51–72 (1996)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Chung, F.-L., Duan, J.-C.: On multistage fuzzy neural network modeling. IEEE Trans. on Fuzzy Systems 8(2), 125–142 (2000)CrossRefGoogle Scholar
  3. 3.
    Furuhashi, T., Matsushita, S., Tsutsui, H., Uchikawa, Y.: Knowledge extraction from hierarchical fuzzy model obtained by fuzzy neural networks and genetic algorithms. In: Proc. IEEE Int. Conf. on Neural Networks (ICNN 1997), Houston, pp. 2374–2379 (1997)Google Scholar
  4. 4.
    Gupta, M.M., Rao, D.H.: On the principles of fuzzy neural networks. Fuzzy Sets and Systems 61, 1–18 (1994)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. on Systems, Man and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Lin, C.-T.: Neural Fuzzy Control Systems with Structure and Parameter Learning. World Scientific, Singapore (1994)Google Scholar
  7. 7.
    Lin, C.-T., Lee, C.S.G.: ‘Neural-network-based fuzzy logic control and decision system. IEEE Trans. on Computers 40(12), 1320–1336 (1991)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Pedrycz, W., Kandel, A., Zhang, Y.-Q.: Neurofuzzy Systems. In: Dubois, D., Prade, H. (eds.) Fuzzy Systems: Modeling and Control, pp. 311–380. Kluwer Academic A Publishers, Dordrecht (1997)Google Scholar
  9. 9.
    Wang, L.-X.: Adaptive Fuzzy Systems and Control Design and Stability Analysis, PTR Prentice Hall (1994)Google Scholar
  10. 10.
    Zhang, Y.-Q., Fraser, M.D., Gagliano, R.A., Kandel, A.: Granular neural networks for numerical-linguistic data fusion and knowledge discovery, Special Issue on Neural Networks for Data Mining and Knowledge Discovery. IEEE Trans. on Neural Networks 11(3), 658–667 (2000)CrossRefGoogle Scholar
  11. 11.
    Zhang, Y.-Q., Kandel, A.: Compensatory Genetic Fuzzy Neural Networks and Their Applications. Series in Machine Perception Artificial Intelligence, vol. 30. World Scientific, Singapore (1998)zbMATHGoogle Scholar
  12. 12.
    Zhang, Y.-Q., Chung, F.: Fuzzy Neural Network Tree with Heuristic Back-propagation Learning. In: Proc. of IJCNN of World Congress on Computational Intelligence 2002, pp. 553–558, Honolulu (2002)Google Scholar
  13. 13.
    Zhang, Y.-Q., Kandel, A.: Compensatory Neurofuzzy Systems with Fast Learning Algorithms. IEEE Trans. on Neural Networks 9(1), 83–105 (1998)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Zhang, Y.-Q.: Constructive Granular Systems with Universal Approximation and Fast Knowledge Discovery. IEEE Trans. on Fuzzy Systems 13(1), 48–57 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Cui Lin
    • 1
  • Jun Li
    • 2
  • Natasha Barrett
    • 3
  • Yan-Qing Zhang
    • 2
  • David A. Washburn
    • 3
  1. 1.Department of Computer Science, Wayne State University, Detroit, MI 48202USA
  2. 2.Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994USA
  3. 3.Department of Psychology, Georgia State University, Atlanta, GA 30303USA

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