Mining the fuzzy control rules of aeration in a submerged biofilm wastewater treatment process

    Authors

    J. C. Chen;N. B. Chang

    Comments

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    Abbreviated Journal Title

    Eng. Appl. Artif. Intell.

    Keywords

    neural networks; fuzzy logic control; hybrid approach; process control; wastewater treatment; ACTIVATED-SLUDGE PROCESS; NEURAL-NETWORK MODEL; TREATMENT-PLANT; LOGIC; CONTROL; SYSTEMS; ALGORITHMS; OPERATION; DYNAMICS; Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic

    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 (O-b) in connection with back propagation neural networks (BPN) algorithm would be the best choice to build up this feedforward fuzzy controller design. (C) 2006 Elsevier Ltd. All rights reserved.

    Journal Title

    Engineering Applications of Artificial Intelligence

    Volume

    20

    Issue/Number

    7

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    959

    Last Page

    969

    WOS Identifier

    WOS:000250473000008

    ISSN

    0952-1976

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