When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.


    Access

    Download


    Export, share and cite



    Title :

    Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm



    Publication date :

    2013-10-15


    Remarks:

    doi:10.24297/ijmit.v6i2.736
    INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY; Vol. 6 No. 2 (2013); 794-804 ; 2278-5612



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 006



    Fault diagnosis using Neuro-Fuzzy Transductive Inference algorithm

    Bo Zhang, / Jianjun Luo, / Zhiqiu Chen, et al. | IEEE | 2008



    Optimizing Multiple Object Tracking and Decision making using neuro-fuzzy

    Chineke Amaechi Hyacenth | BASE | 2019

    Free access

    Intelligent Parallel Parking Using Adaptive Neuro-Fuzzy Inference System Based on Fuzzy C-Means Clustering Algorithm

    Rezaei Nedamani, Hamidreza / Masnadi Khiabani, Parisa / Azadi, Shahram | SAE Technical Papers | 2018


    Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

    Topalov, Andon V. / Oniz, Yesim / Kayacan, Erdal et al. | Tema Archive | 2011