Publication Type : Conference Paper
Thematic Areas : Biotech, Learning-Technologies, Medical Sciences
Publisher : Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India.
Source : Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, Sept 19-22, 2018.
Url : https://ieeexplore.ieee.org/document/8554556
Keywords : Internal model, Kalman filter, Multi-layer perceptron, neural network, trajectory tracking .
Campus : Amritapuri
School : School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : biotechnology, Computational Neuroscience Laboratory
Verified : Yes
Year : 2018
Abstract : Brain circuits in the cerebellum are considered as central processing units of movement control and coordination. Using an internal model controller, it is possible to reconstruct brain like structures that can predict trajectories or reaching arm tasks. In this study, we have developed a bio-inspired neural architecture with unscented and extended Kalman filter optimization methods in order to model complex trajectory kinematics. Employing our previously developed robotic arm, we trained the device to track the trajectory with the perceptron model. The Kalman filter-trained perceptron model achieved prediction-correction process by adding weights to the corresponding synapses in the neurons attributing to error learning by induced plasticity as in neural microcircuits.
Cite this Research Publication : Rajendran A., Abdulsalam A., Mohan D, Thazepurayil J., Prabhat S, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Trajectory tracking using a Bio-inspired neural network for a low cost robotic articulator”, in Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, Sept 19-22, 2018.