Colla, ValentinaVannucci, MarcoMatarese, NicolaStephens, GerardPianezzola, MarcoAlonso, IzaskunLamp, TorstenPalacios, JuanSchiewe, Siegfried2024-07-242024-07-242011Colla , V , Vannucci , M , Matarese , N , Stephens , G , Pianezzola , M , Alonso , I , Lamp , T , Palacios , J & Schiewe , S 2011 , Detection of transients in steel casting through standard and AI-based techniques . in Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings . PART 1 edn , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , no. PART 1 , vol. 6691 LNCS , pp. 256-264 , 11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 , Torremolinos-Malaga , Spain , 8/06/11 . https://doi.org/10.1007/978-3-642-21501-8_32conference97836422150010302-9743https://hdl.handle.net/11556/2164The detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.9enginfo:eu-repo/semantics/restrictedAccessconference output10.1007/978-3-642-21501-8_32industrial problemneuro-fuzzy systemstransient detectionTheoretical Computer ScienceGeneral Computer Sciencehttp://www.scopus.com/inward/record.url?scp=79957944633&partnerID=8YFLogxK