Review of Soft Sensors Methods for Regression Applications F Souza, R Araújo, J Mendes Chemometrics and Intelligent Laboratory Systems 152, 69–79, 2016 | 305 | 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 | 71 | 2013 |
Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm S Rastegar, R Araujo, J Mendes Applied mathematical modelling 45, 606-620, 2017 | 51 | 2017 |
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 | 48 | 2012 |
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 | 47 | 2014 |
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 | 47 | 2013 |
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 | 32 | 2011 |
A review of genetic algorithm approaches for wildfire spread prediction calibration J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo Mathematics 10 (3), 300, 2022 | 30 | 2022 |
Automatic forest fire danger rating calibration: Exploring clustering techniques for regionally customizable fire danger classification JSS Júnior, JR Paulo, J Mendes, D Alves, LM Ribeiro, C Viegas Expert Systems with Applications 193, 116380, 2022 | 24 | 2022 |
Self-tuning PID controllers in pursuit of plug and play capacity J Mendes, L Osório, R Araújo Control Engineering Practice 69, 73-84, 2017 | 24 | 2017 |
Self-evolving fuzzy controller composed of univariate fuzzy control rules J Mendes, R Maia, R Araújo, FAA Souza Applied Sciences 10 (17), 5836, 2020 | 22 | 2020 |
Regenerative braking system modeling by fuzzy Q-Learning R Maia, J Mendes, R Araújo, M Silva, U Nunes Engineering Applications of Artificial Intelligence 93, 103712, 2020 | 22 | 2020 |
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 | 22 | 2017 |
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 | 20 | 2010 |
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 | 19 | 2016 |
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 | 15 | 2014 |
Automatic extraction of the fuzzy control system for industrial processes J Mendes, R Seco, R Araújo ETFA2011, 1-8, 2011 | 15 | 2011 |
Real-time event-driven learning in highly volatile systems: A case for embedded machine learning for scada systems M Goncalves, P Sousa, J Mendes, M Danishvar, A Mousavi Ieee Access 10, 50794-50806, 2022 | 12 | 2022 |
Multicriteria decision-making approach for optimum site selection for off-grid solar photovoltaic microgrids in Mozambique JE Tafula, CD Justo, P Moura, J Mendes, A Soares Energies 16 (6), 2894, 2023 | 11 | 2023 |
Fuzzy model predictive control for nonlinear processes J Menées, R Araújo Proceedings of 2012 IEEE 17th International Conference on Emerging …, 2012 | 11 | 2012 |