Abstract
A multi-step ahead recurrent fuzzy neural network topology is proposed which enables the building of prediction models of nonlinear dynamic processes for heterogeneous model based predictive control. Inputs to the fuzzy neural network are partitioned into several overlapping fuzzy operating regions. Within each region, a simplified linear process model is used. The overall ‘global’ model output is calculated through centre of gravity defuzzification. Process knowledge is used to initially set up the fuzzy network with process input-output data being used to train the network. The new approach is demonstrated by the application to the highly nonlinear pH dynamics in a neutralisation process.
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