An emergency model of home network environment based on genetic algorithm

Authors:
Huey-Ming Lee;Shih-Feng Liao
Affiliations:
Department of Information Management, Chinese Culture University, Yang-Ming-San, Taipei, Taiwan;Department of Information Management, Chinese Culture University, Yang-Ming-San, Taipei, Taiwan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

Citing 5
Cited 3

A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP

Fuzzy Sets and Systems - Special issue on nuclear engineering
Design of adaptive fuzzy controls based on natural control laws

Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases

Fuzzy Sets and Systems
A genetic-algorithm-based method for tuning fuzzy logic controllers

Fuzzy Sets and Systems

Finding abnormal events in home sensor network environment using correlation graph

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A self-adaptive evolutionary negative selection approach for home anomaly events detection

KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A self-tuning emergency model of home network environment

IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we proposed an emergency model of home network environment based on genetic algorithm. This model can not only adapt the home network environment by using genetic algorithm but also detect the emergency events automatically. There are four modules in this model, saying, training knowledge base (TKB), genetic operator module (GOM), emergency knowledge base (EKB), and emergency early warning module (EEWM). TKB receives the messages from the environment and provides them for GOM to train EKB. GOM trains the EKB to fit the real situation by using genetic algorithm. EKB includes the database and the rule base which can provide messages for EEWM to infer. EEWM determines the emergency situations by fuzzy inferences and sends the caution messages to the users by mobile devices. Via this model, our home network environment will become more reliable and safer.