Would the modeling of a physical system that displays a dynamic behavior be benefited by the use of e.g. genetic algorithms in order for optimal temporal decisions to be determined?
The artificial neural networks, as well as other identification models of relationships among variables, could be used to optimize processes helped by behavior rules.
These rules are elaborated by the off-line solution of mono or multiple objective optimization tasks describing decision making processes for different entries data values. Processing the obtained results using neural networks or other models (obtained, by example, using regression analysis methods) it is possible to find models for on line optimization process.
Usually behavior rules are needed for processes operation in real time.
I have elaborated different behavior rules using different models structures, including neural networks.
Hi Georges,
Genetic algorithms gradually leave space for quantum computing algorithms in system control optimization field. Follow:
- J.E. Gough, "Principles and applications of quantum control engineering", 2012 -
Article Principles and applications of quantum control engineering
- Chakrabarti et al., "Quantum Control for Scientists and Engineers", 2010 - http://www.pmc-at.com/pdfs/QC_book.pdf
- Dong et al., "Quantum control theory and applications: A survey", 2011 - https://arxiv.org/pdf/0910.2350.pdf
Regards
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