Abstract
Levinson predictor based Compensatory fuzzy neural networks (LPCFNN), which can be trained by a back-propagation learning algorithm, is proposed as a modeling technique for crude oil distillation processes. This approach adds feedback to the input by using Levinson predictor. Simulation experiments are made by applying proposed LPCFNN on modeling for crude oil distillation process to confirm its effectiveness.
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© 2006 Springer-Verlag Berlin Heidelberg
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He, Y., Fan, Q. (2006). A Levinson Predictor Based Compensatory Fuzzy Neural Network and Its Application in Crude Oil Distillation Process Modeling. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_123
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DOI: https://doi.org/10.1007/11760191_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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