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Advance Prediction of Adverse Digressions in Continuous-Time Systems Using ANN Kernels: A Generic Approach Instantiated in Steel Manufacturing

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 509)


A domain-independent generic methodology is developed for online prediction of rapid adverse digressions in continuous-time systems, preceding the actual incipience of such digressions. The complete methodology itself consists of three stages. The first two stages are domain specific and involve statistical analysis and standard prediction tools like Artificial Neural Networks. The third stage that transforms the ANN outputs into a reliable measure of the instantaneous digression of the system is generic across domains and is the contribution of this work. The core novelty enabling this transformation is a paradigm shift in assessment of the ANN output, from that of “classification” to that of “continuity”. The development of this methodology is performed on a specific industrial process—continuous casting in steel manufacturing—and described in this paper. This can be applied with minor customization to continuous-time systems in domains such as the biological, industrial processes, vehicles, and economic and financial systems.


  • Continuous-time systems
  • Artificial neural networks
  • Classification paradigm
  • Continuity paradigm
  • Adverse digressions
  • Combinatorial relationships

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  1. 1.

    Explanation of NICC:

    The NICC is actually the baseline AI predicted by the ANN over periods of good casting. In the 168 h of the week, it may be assumed that approximately 160 h of good casting is performed. In other words, 95 % of the time casting is “good”. However, the value of AI can drift based on specific casting conditions. Hence, the average AI of last 1 h of casting can be considered as the reference baseline abnormality index of good casting—called Normal Index of Current Casting or NICC. In a different technical domain, the Normality Condition can be extracted based on analogous considerations, with appropriate variation of time window.


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Correspondence to Arya K. Bhattacharya .

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Bhattacharya, A.K., Rajasekar, K. (2017). Advance Prediction of Adverse Digressions in Continuous-Time Systems Using ANN Kernels: A Generic Approach Instantiated in Steel Manufacturing. In: Sahana, S., Saha, S. (eds) Advances in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 509. Springer, Singapore.

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