Related Experiment Video
Updated: Jan 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO
Jian-Sheng Guan1,2, Shao-Jiang Hong1, Shao-Bo Kang1
1College of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, China.
Abstract:
A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for non-linear systems using the genetic particle swarm optimization (GPSO) algorithm is presented in this study. The RARC is used as the principal tracking controller and the robust compensation controller is designed to recover the residual of the approximation error. In the RARC neural network, the steepest descent gradient method and the Lyapunov function are used for deriving the adaptive law parameter of the system. Besides, the learning rates play an important role in these adaptive laws and they have a great effect on the functions of control systems. In this paper, the combination of the genetic algorithm with the mutation particle swarm optimization algorithm is applied to seek for the optimal learning rates of the RARC adaptation laws. The numerical simulations about the inverted pendulum system as well as the robot manipulator system are given to confirm the effectiveness and practicability of the GPSO-RARC-based control system. Compared with other control schemes, the proposed control scheme is testified to be reliable and can obtain the optimal parameter about the learning rates and the minimum root mean square error for non-linear systems.
Related Concept Videos
Linear Circuits
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Linear Momentum
Linearization and Approximation
Network Covalent Solids
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
Application of Linearization and Approximation

