Abstract
This paper concerns the process modelling based on fuzzy logic neural networks. Fuzzy systems are implemented in the form of distributed logic processors. Derivatives required by gradient descent training methods are given, and recursive prediction error training method is used to adjust the model parameters. The approach is illustrated with a modelling example where nitrogen emission (x) data from a fluidized-bed combustion district heating plant is used. The method presented in this paper is general, and can be applied to other complex processes as well.
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