R
Rastko R. Selmic
Researcher at Concordia University
Publications - 120
Citations - 2537
Rastko R. Selmic is an academic researcher from Concordia University. The author has contributed to research in topics: Wireless sensor network & Artificial neural network. The author has an hindex of 23, co-authored 104 publications receiving 2229 citations. Previous affiliations of Rastko R. Selmic include University of Texas at Arlington & Concordia University Wisconsin.
Papers
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Journal ArticleDOI
Deadzone compensation in motion control systems using neural networks
Rastko R. Selmic,Frank L. Lewis +1 more
TL;DR: The technique provides a general procedure for using NNs to determine the preinverse of an unknown right-invertible function and yields tuning algorithms for the weights of the two NNs.
Book
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
TL;DR: From the Publisher: About the Author Frank L. Lewis is Associate Director for Research and Head of the Advanced Controls, Sensors, and MEMS Group at the Automation and Robotics Research Institute at the University of Texas at Arlington.
Journal ArticleDOI
Neural network control of a class of nonlinear systems with actuator saturation
Wenzhi Gao,Rastko R. Selmic +1 more
TL;DR: In this paper, a neural net-based actuator saturation compensation scheme for nonlinear systems in Brunovsky canonical form is presented and rigorously proved and verified using a general "pendulum type" and a robot manipulator dynamical systems.
Journal ArticleDOI
Neural-network approximation of piecewise continuous functions: application to friction compensation
Rastko R. Selmic,Frank L. Lewis +1 more
TL;DR: It is shown here how a certain class of augmented NN, capable of approximating piecewise continuous functions, can be used for friction compensation.
Journal ArticleDOI
Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection
A.I. Moustapha,Rastko R. Selmic +1 more
TL;DR: A dynamic model of wireless sensor networks (WSNs) and its application to a sensor node fault detection based on a new structure of backpropagation-type neural network is presented.