@inproceedings{8d94c954c40e40c394ed2199149f1c8d,
title = "Recurrent fuzzy-neural network with fast learning algorithm for predictive control",
abstract = "This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series - Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.",
keywords = "Gauss-Newton method, Gradient descent, momentum learning, optimization, predictive control, recurrent fuzzy-neural networks, Takagi-Sugeno",
author = "Yancho Todorov and Margarita Terzyiska and Michail Petrov",
year = "2013",
month = oct,
day = "8",
doi = "10.1007/978-3-642-40728-4_58",
language = "English",
isbn = "978-3-642-40727-7",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "459--466",
booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2013 -",
address = "Germany",
note = "23rd International Conference on Artificial Neural Networks, ICANN 2013 ; Conference date: 10-09-2013 Through 13-09-2013",
}