Abstract
The emission of dioxins from waste incinerators is one of the most important environmental problems today. It is known that optimization of waste incinerator controllers is a very difficult problem due to the complex nature of the dynamic environment within the incinerator. In this paper, we propose applying a probabilistically optimal ensemble technique, based on fault masking among individual classifier for N-version programming. We create an optimal ensemble of neural network trained multi-agents and use the majority voting result to predict waste incinerator emission. We show that an optimal ensemble of multi-agents greatly improves the prediction error rate of emission of dioxins.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yamaguchi, D., Mackin, K.J., Tazaki, E. (2005). Waste Incinerator Emission Prediction Using Probabilistically Optimal Ensemble of Multi-agents. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_75
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DOI: https://doi.org/10.1007/11553939_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
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