Fuzzy neural networks for estimation and fuzzy controller design: simulation study for a pulp batch digester

A structural implementation of a fuzzy inference system through connectionist network based on MLP with logical neurons connected through binary and numerical weights is considered. The resulting fuzzy neural network is trained using classical back- propagation to learn the rules of inference of a fuzzy system by adjustment of the numerical weights. For controller design training is carried out o€ line in a closed loop simulation. Rules for the fuzzy logic controller are extracted from the network by interpreting the consequence weights as measure of con®dence of the underlying rule. The framework is used in a simulation study for estima- tion and control of a pulp batch digester. The controlled variable the Kappa number a measure of lignin content in the pulp which is not measurable is estimated through temperature and liquor concentration using the fuzzy neural network. On the other hand a fuzzy neural network is trained to control the Kappa number and rules are extracted from the trained network to construct a fuzzy logic controller.