Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 17, Issue 3, Pages 215-225Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2004.03.004
Keywords
self-organizing system; neural network models; clustering algorithms; monitoring; criterion function; samples; wastewater treatment
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The objective of this project is the development of plant supervision techniques based on self-organizing map (SOM) for the implementation in a wastewater treatment plant. SOM is an unsupervised learning algorithm to establish the relationships among process variables. Clustering techniques such as K-means algorithm have been used for the system state estimation, monitoring and visualization of process states. The best clustering structure is selected by means of the Davies-Bouldin index for evaluation of the several structures obtained from K-means. (C) 2004 Elsevier Ltd. All rights reserved.
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