Exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller

• Autorzy: Pan L.
• Języki publikacji: EN

Abstrakty

EN

To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: 25
• Numer: Special Issue S3
• Opis fizyczny: p.78-83,fig.,ref.

Twórcy

autor

Pan L.

• School of Management, Hefei Uniwersity of Technology, Hefei 230009, China

Bibliografia

• 1. M. Rahmani, and A. Ghanbari, Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator, Vol. 80, pp.117–136, 2016.
• 2. H. N. Nguyen, and J. Zhou, A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network, Vol. 151, pp. 996–1005, 2015.
• 3. W. He, and A. O. David, Neural network control of a robotic manipulator with input dead zone and output constraint,Vol. 46, No. 6, pp. 759–770, 2016.
• 4. M. Beyeler, and N. Oros, A GPU-accelerated cortical neural network model for visually guided robot navigation, Vol. 72, pp. 75–87, 2015.
• 5. P. K. Panigrahi, and S. Ghosh, Navigation of autonomous mobile robot using different activation functions of wavelet neural network, Vol. 25, No. 1, pp. 21–34, 2015.
• 6. I. V. Serban, and A. Sordoni, Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models, Vol. 16, pp. 3776–3784, 2016.
• 7. P. Van Cuong, and W. Y. Nan, Adaptive trajectory tracking neural network control with robust compensator for robot manipulators, Vol. 27, No. 2, pp. 525–536, 2016.
• 8. A. Pandey, and D. R. Parhi, New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture, Vol. 13, No. 2, pp. 129–141, 2016.
• 9. T. Wang, and H. Gao, A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control, Vol. 27, No. 2, pp. 416–425, 2016.
• 10. A. Graves, and G. Wayne, Hybrid computing using a neural network with dynamic external memory, Vol. 538, No. 7626, pp. 471, 2016.
• 11. P. Zamanian, and M. Kasiri, Investigation of Stage Photography in Jee Lee’s Works and Comparing them With the Works of Sandy Skoglund, Acta Electronica Malaysia, Vol. 2, No. 1, pp. 01–06, 2018.
• 12. B. Q. Li, and Z. Li (). Design of Automatic Monitoring System for Transfusion, Acta Electronica Malaysia, Vol. 2, No. 1, pp. 07–10, 2018.
• 13. Z. H. Yan, Modeling and Kinematics Simulation of Plane Ten-Bar Mechanism of Warp Knitting Machine Based on Simcape/Multibody, Acta Mechanica Malaysia, Vol. 2, No. 2, pp. 15–18, 2018.
• 14. A. Abugalia, M. Shaglouf, Analysis of Different Models of Moa Surge Arrester for The Transformer Protection, Acta Mechanica Malaysia, Vol. 2, No. 2, pp. 19–21, 2018.
• 15. F. De’nan, F. Mohamed Nazri, and N. S. Hashim, Finite Element Analysis on Lateral Torsional Buckling Behaviour Oi I-Beam with Web Opening, Engineering Heritage Journal, Vol. 1, No. 2, pp. 19–22, 2017.
• 16. M. A. Hassan, and M. A. Mohd Ismail, Literature Review for The Development of Dikes’s Breach Channel Mechanism Caused by Erosion Processes During Overtopping Failure, Engineering Heritage Journal, Vol. 1, No. 2, pp. 23–30, 2017.

Identyfikatory

DOI

10.2478/pomr-2018-0115