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- W2013224044 abstract "Using a fuzzy neural network (FNN), we constructed a simulation model which estimates the effluent chemical oxygen demand (COD) value from daily routine measurements. Since the water quality of wastewater is changing day by day, an FNN model with a recursively renewing method of learning data (R-FNN) is proposed. With this R-FNN, learning data used to construct an FNN model are renewed with elapsed time so as to estimate the effluent COD value with good accuracy. The estimation results for 9 weeks data using R-FNN were compared with those using a conventional FNN. The average error using the R-FNN model was 0.36 mg/l, while that using the conventional FNN was 1.50 mg/l. Moreover, estimation of the effluent COD throughout one year was carried out, and the average error was only 0.40 mg/l. This result can show the usefulness of the R-FNN for the simulation model of the activated sludge process." @default.
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- W2013224044 date "2001-01-01" @default.
- W2013224044 modified "2023-09-27" @default.
- W2013224044 title "Construction of COD Simulation Model for Activated Sludge Process by Recursive Fuzzy Neural Network." @default.
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- W2013224044 doi "https://doi.org/10.1252/jcej.34.369" @default.
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