MULTILAYER ADAPTIVE NEURAL NETWORK FOR DC LINK VOLTAGE REGULATION IN GRID CONNECTED HYBRID SYSTEMS

2021 ◽  
Vol 41 (3) ◽  
Author(s):  
Masood I. Nazir ◽  
Ikhlaq Hussain ◽  
Aijaz Ahmad
Keyword(s):  
Neural Network ◽  
Hybrid Systems ◽  
Voltage Regulation ◽  
Adaptive Neural Network

The Use of Adaptive Neural Network in a Voice Identification System

2004 ◽  
Vol 62 (11) ◽  
pp. 1005-1010
Author(s):  
O. N. Katkov ◽  
V. A. Pimenov ◽  
A. P. Ryzhkov
Keyword(s):  
Neural Network ◽  
Identification System ◽  
Adaptive Neural Network ◽  
Voice Identification

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

2005 ◽  
Vol 32 (12) ◽  
pp. 3801-3809 ◽  
Author(s):  
Marcus Isaksson ◽  
Joakim Jalden ◽  
Martin J. Murphy
Keyword(s):  
Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Adaptive Neural Network
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