@inproceedings{619b69c1734d47198c57e90cca4de5f8,
title = "Nonlinear control of dynamic systems using single multiplicative neuron models",
abstract = "The paper presents an approach to nonlinear control of dynamic systems using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multilayer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The development of the control algorithm is illustrated through the hardware-in-the-loop (HIL) implementation of DC motor speed control in LabVIEW environment. The controller based on SMN performs better than MLP. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex control systems.",
author = "Turner, {Jonathan G.} and Biswanath Samanta",
year = "2012",
doi = "10.1115/IMECE2012-87440",
language = "English",
isbn = "9780791845202",
series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",
publisher = "American Society of Mechanical Engineers (ASME)",
number = "PARTS A AND B",
pages = "173--181",
booktitle = "Dynamics, Control and Uncertainty",
address = "United States",
edition = "PARTS A AND B",
note = "ASME 2012 International Mechanical Engineering Congress and Exposition, IMECE 2012 ; Conference date: 09-11-2012 Through 15-11-2012",
}