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Applied Intelligence

, Volume 49, Issue 11, pp 3881–3893 | Cite as

A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking

  • Aissa BencherifEmail author
  • Fatima Chouireb
Article
  • 79 Downloads

Abstract

This paper focuses on the design of a recurrent Takagi-Sugeno-Kang interval type-2 fuzzy neural network RTSKIT2FNN for mobile robot trajectory tracking problem. The RTSKIT2FNN is incorporating the recurrent frame of internal-feedback loops into interval type-2 fuzzy neural network which uses simple interval type-2 fuzzy sets in the antecedent part and the Takagi-Sugeno-Kang (TSK) type in the consequent part of the fuzzy rule. The antecedent part forms a local internal feedback loop by feeding the membership function of each node in the fuzzification layer to itself. Initially, the rule base in the RTSKIT2FNN is empty, after that, all rules are generated by online structure learning, and all the parameters of the RTSKIT2FNN are updated online using gradient descent algorithm with varied learning rates VLR. Through experimental results, we demonstrate the effectiveness of the proposed RTSKIT2FNN for mobile robot control.

Keywords

Mobile robot Trajectory tracking Structure and parameter learning Varied learning rates VLR Recurrent type-2 fuzzy neural network RTSKIT2FNN 

Notes

Acknowledgments

The authors would like to thank Telecommunications Signals and Systems Laboratory (TSS) for the support given to this research project

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ElectronicsUniversity Amar TelidjiLaghouatAlgeria

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