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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Bhanot, Surekha | - |
| dc.contributor.author | Mohanta, Hare Krishna | - |
| dc.date.accessioned | 2023-02-09T06:07:10Z | - |
| dc.date.available | 2023-02-09T06:07:10Z | - |
| dc.date.issued | 2013 | - |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/6659349 | - |
| dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9099 | - |
| dc.description.abstract | pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which genetic algorithm is used to optimize the various membership functions of fuzzy inference system. Further, using this optimized fuzzy inference system, adaptive neuro-fuzzy inference system for pH neutralization process is developed. Performances of both control schemes are compared for servo and regulatory operations. Results indicate that adaptive neuro-fuzzy inference system based control uses fewer rules as compared to optimized fuzzy logic based control. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | EEE | en_US |
| dc.subject | pH Neutralization | en_US |
| dc.subject | Fuzzy logic control | en_US |
| dc.subject | Genetic algorithm | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Adaptive neuro-fuzzy inference system | en_US |
| dc.title | Optimized adaptive neuro-fuzzy inference system for pH control | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Department of Electrical and Electronics Engineering | |
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