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Mamdani Model Based Adaptive Neural Fuzzy Inference System and its Application in Traffic Level of Service Evaluation

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3 Author(s)
Yuanyuan Chai ; State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China ; Limin Jia ; Zundong Zhang

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.

Published in:

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on  (Volume:4 )

Date of Conference:

14-16 Aug. 2009