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Improvement of learning of Fuzzified Neural Networks for the Chemical Plant Diagnosis Using a Statistic-based Grade Assignment
Daisaku Kimura, Manabu Nii, Takafumi Yamaguchi and Yutaka Takahashi

We have proposed a fuzzified neural network based failure diagnosis for chemical plants. The proposed method used the fuzzified neural network which is a kind of standard layered neural networks with fuzzy weights and fuzzy biases. For constructing fuzzy models, time series data from process control systems is modified and used as the training data. In order to use the raw time series data as the training data, a reliability grade is assigned to each input-output pair of the raw time series data. Since the training data plays significant role for the suitability, it is important for our method to assign a reliability grade to every input-output pair of the raw time series data. In this paper, we propose a novel grade assignment method for the fuzzified neural network based failure diagnosis.

Keywords: Chemical plants, distributed control systems, fault diagnosis, failure detection, plant operation, fuzzified neural networks, fuzzy nonlinear regression.

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