| Review of Soft Sensors Methods for Regression Applications F Souza, R Araújo, J Mendes Chemometrics and Intelligent Laboratory Systems 152, 69–79, 2016 | 72 | 2016 |
| Adaptive fuzzy identification and predictive control for industrial processes J Mendes, R Araújo, F Souza Expert Systems with Applications 40 (17), 6964-6975, 2013 | 51 | 2013 |
| Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm J Mendes, R Araújo, T Matias, R Seco, C Belchior Engineering Applications of Artificial Intelligence 29, 70-78, 2014 | 31 | 2014 |
| Genetic fuzzy system for data-driven soft sensors design J Mendes, F Souza, R Araújo, N Gonçalves Applied Soft Computing 12 (10), 3237-3245, 2012 | 31 | 2012 |
| An architecture for adaptive fuzzy control in industrial environments J Mendes, R Araújo, P Sousa, F Apóstolo, L Alves Computers in Industry 62 (3), 364-373, 2011 | 27 | 2011 |
| A multilayer-perceptron based method for variable selection in soft sensor design FAA Souza, R Araújo, T Matias, J Mendes Journal of Process Control 23 (10), 1371-1378, 2013 | 21 | 2013 |
| Adaptive fuzzy generalized predictive control based on Discrete-Time TS fuzzy model J Mendes, R Araújo, F Souza 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation …, 2010 | 15 | 2010 |
| Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm S Rastegar, R Araújo, J Mendes Applied Mathematical Modelling 45, 606-620, 2017 | 14 | 2017 |
| A new approach for online TS fuzzy identification and model predictive control of nonlinear systems S Rastegar, R Araújo, J Mendes Journal of Vibration and Control 22 (7), 1820-1837, 2016 | 11 | 2016 |
| Self-tuning PID controllers in pursuit of plug and play capacity J Mendes, L Osório, R Araújo Control Engineering Practice 69, 73-84, 2017 | 9 | 2017 |
| Fuzzy model predictive control for nonlinear processes J Menées, R Araújo Proceedings of 2012 IEEE 17th International Conference on Emerging …, 2012 | 8 | 2012 |
| Automatic extraction of the fuzzy control system for industrial processes J Mendes, R Seco, R Araújo ETFA2011, 1-8, 2011 | 8 | 2011 |
| A novel robust control scheme for LTV systems using output integral discrete-time synergetic control theory S Rastegar, R Araujo, J Sadati, J Mendes European Journal of Control 34, 39-48, 2017 | 7 | 2017 |
| Evolutionary learning of a fuzzy controller for industrial processes J Mendes, R Araújo, T Matias, R Seco, C Belchior IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society …, 2014 | 6 | 2014 |
| A comparison of adaptive PID methodologies controlling a DC motor with a varying load L Osório, J Mendes, R Araújo, T Matias 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation …, 2013 | 6 | 2013 |
| Adaptive predictive control with recurrent fuzzy neural network for industrial processes J Mendes, N Sousa, R Araújo ETFA2011, 1-8, 2011 | 5 | 2011 |
| Online evolving fuzzy control design: An application to a CSTR plant J Mendes, F Souza, R Araújo 2017 IEEE 15th International Conference on Industrial Informatics (INDIN …, 2017 | 4 | 2017 |
| Variable selection based on mutual information for soft sensors applications F Souza, R Araújo, S Soares, J Mendes 9 th Portuguese Conference on Automatic Control, Coimbra, 2010 | 4 | 2010 |
| Neo-fuzzy neuron learning using backfitting algorithm J Mendes, F Souza, R Araújo, S Rastegar Neural Computing and Applications 31 (8), 3609-3618, 2019 | 2 | 2019 |
| Computational Intelligence Methodologies for Control of Industrial Processes JAP Mendes | 2 | 2014 |
Rui AraújoUniversity of CoimbraEmail confirmado em isr.uc.pt
