Title

Mining The Fuzzy Control Rules Of Aeration In A Submerged Biofilm Wastewater Treatment Process

Keywords

Fuzzy logic control; Hybrid approach; Neural networks; Process control; Wastewater treatment

Abstract

This paper presents a special rule base extraction analysis for optimal design of an integrated neural-fuzzy process controller using an "impact assessment approach." It sheds light on how to avoid some unreasonable fuzzy control rules by screening inappropriate fuzzy operators and reducing over fitting issues simultaneously when tuning parameter values for these prescribed fuzzy control rules. To mitigate the design efforts, the self-learning ability embedded in the neural networks model was emphasized for improving the rule extraction performance. An aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. Four different fuzzy operators were compared against one other in terms of their actual performance of automated knowledge acquisition in the system based on a partial or full rule base prescribed. Research findings suggest that using bounded difference fuzzy operator (Ob) in connection with back propagation neural networks (BPN) algorithm would be the best choice to build up this feedforward fuzzy controller design. © 2006 Elsevier Ltd. All rights reserved.

Publication Date

10-1-2007

Publication Title

Engineering Applications of Artificial Intelligence

Volume

20

Issue

7

Number of Pages

959-969

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.engappai.2006.11.012

Socpus ID

34548683431 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34548683431

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