Mamdani model based adaptive neural fuzzy inference system and its application in traffic level of service evaluation

Authors:
Yuanyuan Chai;Limin Jia;Zundong Zhang
Affiliations:
State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Year:
2009

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Abstract

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service Show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.