An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process

The real-time availability of key water quality parameters is of great importance for an advanced and optimized process control in wastewater treatment plants (WWTPs). However, due to the complex environment conditions and costly measuring instruments, it is generally difficult and time-consuming to...

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Vydáno v:	Neural computing & applications Ročník 33; číslo 17; s. 11401 - 11414
Hlavní autoři:	Meng, Xi, Zhang, Yin, Qiao, Junfei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.09.2021 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Artificial neural networks Biochemical oxygen demand Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Effluents Error correction Image Processing and Computer Vision Machine learning Mathematical models Measuring instruments Original Article Parameters Prediction models Probability and Statistics in Computer Science Process controls Radial basis function Time measurement Wastewater treatment Water quality Water treatment Radial basis function (RBF) networks Adaptive task-oriented Water quality parameters prediction Second-order algorithm
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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