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Generation of TPMACA for Pattern Classification

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Cellular Automata (ACRI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8751))

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Abstract

The important prerequisites of designing pattern classifier are high throughput and low cost hardware implementation. The simple, regular, modular and cascadable local neighborhood sparse network of Cellular Automata (CA) suits ideally for low cost VLSI implementation. Thus the multiple attractor CA is adapted for use as a pattern classifier. By concatenating two predecessor multiple attractor CA (TPMACA) we can construct a pattern classifier. In this paper we propose a method for finding dependency vector by using a 0-basic path. Also we propose various methods for generating TPMACA corresponding to a given dependency vector.

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A5B6053791).

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Pukyong National University, Busan, 608-737, Korea

    Sung-Jin Cho, Jin-Gyoung Kim, Sook-Hee Kwon & Gil-Tak Gong

  2. Institute of Basic Sciences and Department of Applied Mathematics, Inje University, Gimhae, 621-749, Korea

    Han-Doo Kim

  3. Department of Information and Communications Engineering, Tongmyoung University, Busan, 608-711, Korea

    Un-Sook Choi

  4. Department of Information and Communications Engineering, Pukyong National University, Busan, 608-737, Korea

    Seok-Tae Kim

Authors
  1. Sung-Jin Cho
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  2. Han-Doo Kim
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  3. Un-Sook Choi
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  4. Seok-Tae Kim
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  5. Jin-Gyoung Kim
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  6. Sook-Hee Kwon
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  7. Gil-Tak Gong
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Editor information

Editors and Affiliations

  1. Dept. of Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Krakow, Poland

    Jarosław Wąs

  2. Dept. of Electrical and Computer Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Georgios Ch. Sirakoulis

  3. CSAI - Complex Systems and Artificial Intelligence Research Center, University of Milano-Bicocca, 20126, Milan, Italy

    Stefania Bandini

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© 2014 Springer International Publishing Switzerland

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Cho, SJ. et al. (2014). Generation of TPMACA for Pattern Classification. In: Wąs, J., Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2014. Lecture Notes in Computer Science, vol 8751. Springer, Cham. https://doi.org/10.1007/978-3-319-11520-7_42

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  • DOI: https://doi.org/10.1007/978-3-319-11520-7_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11519-1

  • Online ISBN: 978-3-319-11520-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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