Robust Stabilization of Nonlinear Fractional Order Interconnected Systems Based on T-S Fuzzy Model

Li, Yuting

doi:10.1155/2020/5386960

Cited by 1 publication

(1 citation statement)

References 40 publications

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“…Yang et al [35] used different samples to train a T-S fuzzy neural network, and the results showed that the composition and quantity of training samples had an important impact on water quality evaluation. Li [36] used a T-S fuzzy neural network to study the robust stabilization of nonlinear fractional-order interconnected systems. Song et al [37] focused on the state estimation issue of T-S fuzzy Markovian generalized neural networks with reaction-diffusion terms.…”

Section: Application Of T-s Fuzzy Neural Network In Other Fieldsmentioning

confidence: 99%

Speed Grade Evaluation of Public-Transportation Lines Based on an Improved T-S Fuzzy Neural Network

Zhang

Zhong³

et al. 2020

Journal of Advanced Transportation

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This paper proposes an evaluation method based on a T-S fuzzy neural network for evaluating the speed grade of public-transport lines in the context of large-scale rail-transit planning and construction in Hangzhou. The six-dimensional data of morning peak/evening peak average speed, average speed at peak, average station distance, proportion of dedicated lanes, and nonlinear coefficients were selected as input data for the neural network to output the operating speed grade of bus lines. Improving and optimizing the membership function of the Takagi–Sugeno (T-S) model improves its predicted result accuracy compared to a traditional T-S model. The line data of 28 typical trunk lines or expressways in Hangzhou were used as an example; the results demonstrate that the speed grade evaluation method based on an improved T-S fuzzy neural network can effectively and quickly evaluate the speed grade of Hangzhou public-transportation lines. This paper presents a novel analysis and method for large-scale rail-transit planning and evaluation of urban public-transport lines. The aim is to provide practical instruction for the subsequent optimization of public-transportation lines in Hangzhou.

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Section: Application Of T-s Fuzzy Neural Network In Other Fieldsmentioning

confidence: 99%