<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5810" mdate="2009-03-27 00:00:00"> <author>Yuanyuan Chai and Limin Jia and Zundong Zhang</author> <title>Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application</title> <pages>663 - 670</pages> <year>2009</year> <volume>3</volume> <number>3</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/5810</ee> <url>https://publications.waset.org/vol/27</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From indepth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (MANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. MANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying MANFIS to evaluate traffic Level of service show that MANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in nonlinear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.</abstract> <index>Open Science Index 27, 2009</index> </article>