Fault Tolerance for Small-World Cellular Neural Networks

  • Authors:

  • Kautsuyoshi Matsumoto;Minoru Uehara;Hideki Mori

  • Affiliations:

  • Dept. of Open Information Systems, Graduate School of Engineering, Toyo University, Saitama, Japan;Dept. of Open Information Systems, Graduate School of Engineering, Toyo University, Saitama, Japan;Dept. of Open Information Systems, Graduate School of Engineering, Toyo University, Saitama, Japan

  • Venue:
  • NBiS '08 Proceedings of the 2nd international conference on Network-Based Information Systems
  • Year:
  • 2008

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Abstract

In this paper, we propose a mechanism for fault tolerance for Small-World Cellular Neural Networks (SWCNN). Small-world networks exist in the range between regular and random networks. SWCNN is a Cellular Neural Network (CNN) that has small-world network structure and has better performance than CNN. SWCNN needs to be fault tolerant because it has higher levels of error propagation than CNN.