Abstract
This paper proposes the use of error back proragation (BP) neural network to efficiently control the pH in the clarifying process of sugar cane juice. In particular approximate dynamic programming (ADP) is implemented to solve this nonlinear control problem. The neural network model of the clarifying process of sugar cane juice and a neural network controller based on the idea of ADP to achieve optimal control are developed. The strategy and training procedures of dual heuristic programming (DHP) are discussed. The result is the “plant” has been effectively controlled using DHP.
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Lin, X., Teng, Q., Song, C., Song, S., Liu, H. (2010). PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_38
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DOI: https://doi.org/10.1007/978-3-642-14922-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
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