Convergence of Gradient Method With Momentum for Two-Layer Feedforward Neural Networks

2006 ◽  
Vol 17 (2) ◽  
pp. 522-525 ◽  
Author(s):  
N. Zhang ◽  
W. Wu ◽  
G. Zheng
Keyword(s):  
Neural Networks ◽  
Gradient Method ◽  
Feedforward Neural Networks

scholarly journals Deterministic convergence of chaos injection-based gradient method for training feedforward neural networks

2015 ◽  
Vol 9 (3) ◽  
pp. 331-340 ◽  
Author(s):  
Huisheng Zhang ◽  
Ying Zhang ◽  
Dongpo Xu ◽  
Xiaodong Liu
Keyword(s):  
Neural Networks ◽  
Gradient Method ◽  
Feedforward Neural Networks

Neural Networks Predictive Controller Using an Adaptive Control Rate

2016 ◽  
pp. 614-633 ◽  
Author(s):  
Ahmed Mnasser ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Neural Networks ◽  
Gradient Method ◽  
Feedforward Neural Networks ◽  
Neural Model ◽  
Asymptotic Convergence ◽  
Marquardt Method ◽  
Predictive Controller ◽  
The Neural Networks ◽  
The Stability ◽  
Unknown Nonlinear Dynamics

A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

Neural Networks Predictive Controller Using an Adaptive Control Rate

2014 ◽  
Vol 3 (3) ◽  
pp. 127-147 ◽  
Author(s):  
Ahmed Mnasser ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Neural Networks ◽  
Gradient Method ◽  
Feedforward Neural Networks ◽  
Neural Model ◽  
Closed Loop System ◽  
Variable Control ◽  
Predictive Controller ◽  
The Neural Networks ◽  
The Stability ◽  
Unknown Nonlinear Dynamics

A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

Sign in / Sign up

Export Citation Format

Share Document