publications

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2012
Nascimento, I., Jardim R., & Morgado D. F. (2012).  A new solution to the hyperbolic tangent implementation in hardware: polynomial modeling of the fractional exponential part. Neural Computing and Applications, [Impact Factor: 1.168]. 1-7.
Abreu, S. Rodrigues, Barreto C., & Morgado D. F. (2012).  Renewable Energy Characterization of Madeira Island. Controlo’2012.
Reis, L., Aguiar L., Baptista D., & Morgado D. F. (2012).  A Software Tool for Automatic Generation of Neural Hardware. International Arab Journal of Information Technology.
2011
Reis, L., Aguiar L., Baptista D., & Morgado D. F. (2011).  ANGE – Automatic Neural Generator. (Honkela, T., Duch W., Girolami M., & Kaski S., Ed.).Artificial Neural Networks and Machine Learning – ICANN 2011. 6792, 446-453.
2010
Morgado, D. F., & Antunes A. (2010).  Fault Tolerance of Artificial Neural Networks: an Open Discussion for a Global Model. International Journal of Circuits, Systems and Signal Processing [Impact Factor SJR2012: 0.201]. 4(1), 9-16.
Morgado, D. F., Borralho R., Fontes P., & Antunes A. (2010).  FTSET-a software tool for fault tolerance evaluation and improvement. Neural Computing and Applications [Impact Factor SJR2010: 0.291]. 19, 701-712.
Antunes, A., Morgado D. F., & Mota J. Vieira and (2010).  Implementation of the Delay Compensator Approach, Factory Automation. (Silvestre-Blanes, J., Ed.).Factory Automation.
Aguiar, L., Reis L., & Morgado D. F. (2010).  Neuron Implementation using System Generator. 9th Portuguese Conference on Automatic Control.
2009
Hoelzle, G., & Morgado D. F. (2009).  Hardware Implementation of an Artificial Neural Network with an Embedded Microprocessor in a FPGA. 8th International Conference and Workshop on Ambient Intelligence and Embedded Systems.
Borralho, R., Fontes P., Antunes A., & Morgado D. F. (2009).  A Matlab Tool for Analyzing and Improving Fault Tolerance of Artificial Neural Networks. 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing.
2008
Antunes, A., Morgado D. F., & Mota A. (2008).  Comparing the delay compensator approach with fractional dead-time modelling in distributed real-time control systems. 8th Portuguese Conference on Automatic Control.
Morgado, D. F., & Antunes A. (2008).  An empirical study of the test error versus training error in artificial neural networks. Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information. 266–271.
Morgado, D. F., & Antunes A. (2008).  Fault Tolerance Improvement through Architecture Change in Artificial Neural Networks. Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence. 248–257.
Fontes, P., Borralho R., Antunes A., & Morgado D. F. (2008).  Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks. 8th Portuguese Control Conference.
Morgado, D. F., & Antunes A. (2008).  A global model for fault tolerance of feedforward neural networks. Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information. 272–275.
Antunes, A., Morgado D. F., & Mota A. (2008).  A Neural Model for Delay Correction in a Distributed Control System. (Kůrková, V., Neruda R., & Koutník J., Ed.).Artificial Neural Networks - ICANN 2008. 5164, 11-20.
Morgado, D. F., & Antunes A. (2008).  Test error versus training error in artificial neural networks for systems affected by noise. International Journal of System Applications, Engineering and Development. 2(3), 83-90.
2007
Ferreira, P., Ribeiro P., Antunes A., & Morgado D. F. (2007).  A high bit resolution FPGA implementation of a FNN with a new algorithm for the activation function. Neurocomputing. 71, 71 - 77.
2006
Antunes, A., Morgado D. F., Vieira J., & Mota A. (2006).  Delay Compensator: an approach to reduce the variable sampling to actuation delay effect in distributed real-time control systems. 11th IEEE International Conference on Emerging Technologies and Factory Automation.
Antunes, A., Morgado D. F., & Mota A. Manuel (2006).  Improving the control performance of a real-time distributed control system under variable sampling to actuation delay. Proceedings of the 25th IASTED international conference on Modeling, indentification, and control. 161–166.
Morgado, D. F., Antunes A., Vieira J., & Mota A. (2006).  On-Line Learning of a Time Variant System. (Ali, M., & Dapoigny R., Ed.).Advances in Applied Artificial Intelligence. 4031, 908-916.
Morgado, D. F., & Antunes A. (2006).  Physical implementation of neural networks. (Lalic, B., Ed.).Advanced Technologies: Research, Development & Applications.
Morgado, D. F., Antunes A., Vieira J., & Mota A. (2006).  A sliding window solution for the on-line implementation of the Levenberg-Marquardt algorithm. Eng. Appl. Artif. Intell.. 19, 1–7.
2005
Morgado, D. F., Antunes A., & Mota A. (2005).  Additive Internal Model Control: a new Control Strategy. International Transactions on Computer Science and Engineering. 25(1), 1-12.
Antunes, A., Morgado D. F., & Mota A. (2005).  Fractional dead-time modeling for variable sampling to actuation delay compensation in distributed real-time distributed control systems. International Transactions on Computer Science and Engineering. 24(1), 9-18.

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