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A pattern recognition system using evolvable hardware

  • Applications of Evolutionary Computation Evolutionary Computation in Machine Learning, Neural Networks, and Fuzzy Systems
  • Conference paper
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Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

We describe a high-speed pattern recognition system using Evolvable Hardware (EHW), which can change its own hardware structure by genetic learning in order to adapt best to the environment. The purpose of the system is to show that EHW can work as a recognition device with such robustness for the noise as seen in the recognition systems based on neural networks. The advantage of EHW compared with a neural network is the high processing speed and the readability of the learned result. The readability means that the result is understandable in terms of Boolean functions. In this paper, we describe the architecture, the learning algorithm and the experiment on the pattern recognition system using EHW.

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References

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Author information

Authors and Affiliations

  1. Electrotechnical Laboratory, 1-1-4,Umezono,Tsukuba, 305, Ibaraki, Japan

    Masaya Iwata, Hitoshi Iba & Tetsuya Higuchi

  2. University of Tsukuba, 1-1-1,Tennoudai,Tsukuba, 305, Ibaraki, Japan

    Isamu Kajitani & Hitoshi Yamada

Authors
  1. Masaya Iwata
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  2. Isamu Kajitani
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  3. Hitoshi Yamada
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  4. Hitoshi Iba
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  5. Tetsuya Higuchi
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Iwata, M., Kajitani, I., Yamada, H., Iba, H., Higuchi, T. (1996). A pattern recognition system using evolvable hardware. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1039

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  • DOI: https://doi.org/10.1007/3-540-61723-X_1039

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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