A Self-tuning Emergency Model of Home Network Environment

  • Huey-Ming Lee
  • Shih-Feng Liao
  • Tsang-Yean Lee
  • Mu-Hsiu Hsu
  • Jin-Shieh Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


In this paper, we proposed a self-tuning emergency model of home network environment (SEMHNE). This model can not only tune the scaling factors and membership functions to fit the home network environment but also detect the emergency events automatically. There are three modules in this model, namely, emergency report module (ERM), renewable emergency rule base (RERB), and evolutionary database (EDB). ERM determines the emergency situations by fuzzy inferences and sends the warning message to the users. RERB can provide rules to ERM for inference. EDB can do self-tuning by using genetic algorithm and provide information to ERM for inference. Via this model, our home network environment will become more reliable and safety.


Genetic Algorithm Membership Function Scaling Factor Fuzzy Neural Network Home Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huey-Ming Lee
    • 1
  • Shih-Feng Liao
    • 1
  • Tsang-Yean Lee
    • 1
  • Mu-Hsiu Hsu
    • 1
  • Jin-Shieh Su
    • 1
  1. 1.Department of Information ManagementChinese Culture UniversityTaipeiTaiwan

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