Abstract
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.
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References
Chen, H.P., Parng, T.M.: A new approach of multi-stage fuzzy logic inference. Fuzzy Sets and Systems 78, 51–72 (1996)
Chung, F.-L., Duan, J.-C.: On multistage fuzzy neural network modeling. IEEE Trans. on Fuzzy Systems 8(2), 125–142 (2000)
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)
Gupta, M.M., Rao, D.H.: On the principles of fuzzy neural networks. Fuzzy Sets and Systems 61, 1–18 (1994)
Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. on Systems, Man and Cybernetics 23(3), 665–685 (1993)
Lin, C.-T.: Neural Fuzzy Control Systems with Structure and Parameter Learning. World Scientific, Singapore (1994)
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)
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)
Wang, L.-X.: Adaptive Fuzzy Systems and Control Design and Stability Analysis, PTR Prentice Hall (1994)
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)
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)
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)
Zhang, Y.-Q., Kandel, A.: Compensatory Neurofuzzy Systems with Fast Learning Algorithms. IEEE Trans. on Neural Networks 9(1), 83–105 (1998)
Zhang, Y.-Q.: Constructive Granular Systems with Universal Approximation and Fast Knowledge Discovery. IEEE Trans. on Fuzzy Systems 13(1), 48–57 (2005)
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Lin, C., Li, J., Barrett, N., Zhang, YQ., Washburn, D.A. (2007). Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Pattern Recognition. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds) Web Intelligence Meets Brain Informatics. WImBI 2006. Lecture Notes in Computer Science(), vol 4845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77028-2_15
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DOI: https://doi.org/10.1007/978-3-540-77028-2_15
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