Tao Gao

Orcid: 0000-0002-5547-7370

Affiliations:
  • Beihang University, School of Computer Science and Engineering, Beijing, China
  • China University of Petroleum, College of Science, College of Control Science and Engineering, Qingdao, China


According to our database1, Tao Gao authored at least 12 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
A smoothing Group Lasso based interval type-2 fuzzy neural network for simultaneous feature selection and system identification.
Knowl. Based Syst., November, 2023

2022
A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis.
Pattern Recognit., 2022

Sensitivity analysis of Takagi-Sugeno fuzzy neural network.
Inf. Sci., 2022

2021
A modified conjugate gradient-based Elman neural network.
Cogn. Syst. Res., 2021

Feature Selection for Fuzzy Neural Networks using Group Lasso Regularization.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

2020
A recalling-enhanced recurrent neural network: Conjugate gradient learning algorithm and its convergence analysis.
Inf. Sci., 2020

2019
Conjugate gradient-based Takagi-Sugeno fuzzy neural network parameter identification and its convergence analysis.
Neurocomputing, 2019

An Improved Ant Colony Optimization Algorithm Based on Fractional Order Memory for Traveling Salesman Problems.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

2018
A Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and its Convergence.
IEEE Access, 2018

A New Parameter Identification Method for Type-1 TS Fuzzy Neural Network.
Proceedings of the Advances in Neural Networks - ISNN 2018, 2018

An Efficient Elman Neural Networks Based on Improved Conjugate Gradient Method with Generalized Armijo Search.
Proceedings of the Intelligent Computing Theories and Application, 2018

2017
An Improved Conjugate Gradient Neural Networks Based on a Generalized Armijo Search Method.
Proceedings of the Neural Information Processing - 24th International Conference, 2017


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