Tehnički vjesnik, Vol. 25 No. 2, 2018.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20161025220853
Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization
Ramazan Coban
orcid.org/0000-0002-4505-0437
; Department of Computer Engineering, Cukurova University, 01330 Balcali, Saricam / Adana / Turkey
Inayet Ozge Aksu
; Department of Computer Engineering, Adana Science and Technology University, Gültepe Mahallesi, Çatalan Caddesi No:201/5, 01250 Sarıçam / Adana / Turkey
Sažetak
In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network’s output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems.
Ključne riječi
hard disk drive; neuro-control; particle swarm optimization; recurrent neural networks
Hrčak ID:
199141
URI
Datum izdavanja:
21.4.2018.
Posjeta: 1.753 *