A Levinson Predictor Based Compensatory Fuzzy Neural Network and Its Application in Crude Oil Distillation Process Modeling
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.
KeywordsMembership Function Simulation Error Rule Layer Step Step Output Action Strength
Unable to display preview. Download preview PDF.
- 3.Sun, W., Wang, Y.: A Recurrent Neural Network Based Adaptive Control and Its Application on Robotic Tracking Control. Neural Information Processing 5(1), 19–26 (2004)Google Scholar
- 6.Zhang, X.D.: Modern Signal Processing. Tsinghua University Press and Springer Press, Beijing (2002)Google Scholar