Development and characterisation of C???Mn???Al???Si???Nb TRIP aided steel, Materials Science and Engineering: A, vol.528, issue.6, pp.2394-2400, 2011. ,
DOI : 10.1016/j.msea.2010.11.054
Neural Networks for Optimization and Signal Processing, 1993. ,
Predicting of mechanical properties of Fe???Mn???(Al, Si) TRIP/TWIP steels using neural network modeling, Computational Materials Science, vol.45, issue.4, pp.959-965, 2009. ,
DOI : 10.1016/j.commatsci.2008.12.015
Modeling the yield strength of hot strip low carbon steels by artificial neural network, Materials & Design, vol.30, issue.9, pp.3653-3658, 2009. ,
DOI : 10.1016/j.matdes.2009.02.018
Optimal Control of Batch Biotechnological Processes using Neural Network Model, 9th Int. Conf. Systems for Automation of Engineering and Research, pp.95-99, 1995. ,
Neural Network Optimization of Initial Conditions of Milk Starter Culture Cultivation, Special Issue on Innovations in Intelligent Systems and Applications of the International Journal of Reasoning-based Intelligent Systems, pp.285-292, 2010. ,
Neural Networks for Mechanical Characteristics Modeling and Compositions Optimization of Steel Alloys, Int. Conf. Automatic and Informatics, pp.49-52, 2010. ,
Software products for modelling and simulation in materials science, Computational Materials Science, vol.28, issue.2, pp.179-198, 2003. ,
DOI : 10.1016/S0927-0256(03)00106-X
Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network, Computational Materials Science, vol.21, issue.3, pp.375-394, 2001. ,
DOI : 10.1016/S0927-0256(01)00160-4
Neural networks for self-learning control systems, International Journal of Control, vol.1, issue.6, pp.1439-1451, 1991. ,
DOI : 10.1080/00207179108934220
Backpropagation through time: what it does and how to do it, Proceedings of the IEEE, vol.78, issue.10, pp.1550-1560, 1990. ,
DOI : 10.1109/5.58337
An overview of neural networks for control, IEEE Control Systems, vol.11, issue.1, pp.40-41, 1991. ,
DOI : 10.1109/37.103352