A Self-tuning Emergency Model of Home Network Environment
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.
KeywordsGenetic Algorithm Membership Function Scaling Factor Fuzzy Neural Network Home Network
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