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Fuzzy Neural Networks

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Feature Extraction

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 207))

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Abstract

The theory of fuzzy logic, founded by Zadeh (1965), deals with the linguistic notion of graded membership, unlike the computational functions of the digital computer with bivalent propositions. Since mentation and cognitive functions of brains are based on relative grades of information acquired by the natural (biological) sensory systems, fuzzy logic has been used as a powerful tool for modeling human thinking and cognition (Gupta and Sinha, 1999, Gupta et al., 2003). The perceptions and actions of the cognitive process thus act on the graded information associated with fuzzy concepts, fuzzy judgment, fuzzy reasoning, and cognition. The most successful domain of fuzzy logic has been in the field of feedback control of various physical and chemical processes such as temperature, electric current, flow of liquid/gas, and the motion of machines (Gupta, 1994, Rao and Gupta, 1994, Sun and Jang, 1993, Gupta and Kaufmann, 1988, Kiszka et al., 1985, Berenji and Langari, 1992, Lee, 1990a,b). Fuzzy logic principles can also be applied to other areas. For example, these fuzzy principles have been used in the area such as fuzzy knowledge-based systems that use fuzzy IF-THEN rules, fuzzy software engineering, which may incorporate fuzziness in data and programs, and fuzzy database systems in the field of medicine, economics, and management problems. It is exciting to note that some consumer electronic and automotive industry products in the current market have used technology based on fuzzy logic, and the performance of these products has significantly improved (Al-Holou et al., 2002, Eichfeld et al., 1996).

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Author information

Authors and Affiliations

  1. Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, Saskatchewan, Canada, S7N 5A9

    Madan M. Gupta

  2. School of Health Sciences, Faculty of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Japan, 980-8575

    Noriyasu Homma

  3. Institute of Automation, The Chinese Academy of Sciences, P. O. Box 2728, Beijing, P.R. China, 100080

    Zeng-Guang Hou

Authors
  1. Madan M. Gupta
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  2. Noriyasu Homma
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  3. Zeng-Guang Hou
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Editor information

Editors and Affiliations

  1. Clopinet, 955 Creston Road, 94708, Berkeley, USA

    Isabelle Guyon

  2. Department of Electrical Engineering & Computer Science — EECS, University of California, 94720, Berkeley, USA

    Masoud Nikravesh

  3. School of Electronics and Computer Sciences, University of Southampton, SO17 1BJ, Southampton Highfield, UK

    Steve Gunn

  4. Division of Computer Science Lab. Electronics Research, University of California, Soda Hall 387, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

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© 2006 Springer-Verlag Berlin Heidelberg

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Gupta, M.M., Homma, N., Hou, ZG. (2006). Fuzzy Neural Networks. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds) Feature Extraction. Studies in Fuzziness and Soft Computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_9

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  • DOI: https://doi.org/10.1007/978-3-540-35488-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35487-1

  • Online ISBN: 978-3-540-35488-8

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