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Paper Details

Paper Title
IMPROVING PREDICTION ACCURACY OF THE TRAFFIC SPEED USING THE FUZZY NEURAL NETWORK
Abstract
The accuracy of the predicted speed is the more important in the efficiency of the traffic management system. The first-order Takagi–Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN), two learning processes are proposed. First, a K-means method is employed to partition input samples into different clusters and a Gaussian fuzzy membership function was designed for each cluster to measure the membership degree of the samples to the cluster centers. When the number of the input samples increases, the cluster centers are modified and membership functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi–Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.
KeyWord
cluster, Gaussian, K-means method, weighted recursive least squares estimator, periodic pattern.
Others Details
Paper Id : 82154
Author Name : K.Vijiyakumar
Co-Author Name(s) : K.ArunK.RajeshkumarN.Thamizhmuthalvan
Volume/Issue No : Volume 05 Issue 04
Page No : 175-182
DOI Number : DOI:10.21090/IJAERD.82154
Publication Date : 2018-04-07
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website : http://www.ijaerd.com/index.php
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406