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System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

Tsung-Chih Lin, Yi-Ming Chang, Tun-Yuan Lee
Copyright: © 2011 |Volume: 1 |Issue: 3 |Pages: 20
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781613507070|DOI: 10.4018/ijfsa.2011070105
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MLA

Lin, Tsung-Chih, et al. "System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network." IJFSA vol.1, no.3 2011: pp.66-85. http://doi.org/10.4018/ijfsa.2011070105

APA

Lin, T., Chang, Y., & Lee, T. (2011). System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network. International Journal of Fuzzy System Applications (IJFSA), 1(3), 66-85. http://doi.org/10.4018/ijfsa.2011070105

Chicago

Lin, Tsung-Chih, Yi-Ming Chang, and Tun-Yuan Lee. "System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network," International Journal of Fuzzy System Applications (IJFSA) 1, no.3: 66-85. http://doi.org/10.4018/ijfsa.2011070105

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Abstract

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

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