A new hybrid algorithm combining a new chaos optimization approach with gradient descent for high dimensional optimization problems

Aslimani, Nassime; Ellaia, Rachid

doi:10.1007/s40314-017-0454-9

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…According to Equation (29), the centre and width can be updated by the gradient descent [27,28]. The gradient descent linear function of eðkÞ can be expressed as…”

Section: International Journal Of Aerospace Engineeringmentioning

confidence: 99%

Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

Liu

Bao

Xue

et al. 2019

International Journal of Aerospace Engineering

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This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

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“…According to Equation (29), the centre and width can be updated by the gradient descent [27,28]. The gradient descent linear function of eðkÞ can be expressed as…”

Section: International Journal Of Aerospace Engineeringmentioning

confidence: 99%