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process/patterns prevents a 100% prediction accuracy, therefore the errors of the
intelligent systems can be accepted at no cost;
modelling of complex systems
, where any other modelling technique would be as
incomprehensible as a neurofuzzy model;
consumer applications
where the appeal of the “fuzzy label” increases the market of an
appliance.
6. References
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Allotta, B; Colla, V.; Malvezzi, M. (2002). Train Position And Speed Estimation Using Wheel
Velocity Measurements
Journal of Rail and Rapid Transit Proceedings of the Institution
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Colla, V.; Reyneri, L.M.; Sgarbi, M. (2000). Parametric Characterization of Jominy Profiles in
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Colla, V.; Vannucci, M.; Allotta, B.; Malvezzi, M. (2003). Comparison of traditional and
neural system for train speed estimation,
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th
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on Artificial Neural Networks ESANN 2003
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EOSC'06
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Colla, V.; Nastasi, G. (2010). Modelling and Simulation of an Automated Warehouse for the
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Modelling, Simulation and
Optimization
, INTECH pp. 471-486 (ISBN 978-953-7619-36-7).
Colla, V.; Nastasi, G.; Matarese, N.; Reyneri L.M. (2010). GA-Based Solutions Comparison
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Development of a model predicting inclusions precipitation in nozzles based on
chemical composition and process parameters such as casting rate, liquid
temperature, nozzle design and slag composition,
European Commission Ed.
Technical Report EUR 21442.
Haykin, S. (1994), Neural Networks: A Comprehensive Foundation,
Mc Millan College
Publishing Company
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casting,
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