The use of an artificial neural network to improve precision in trace level, quantitative analysis of heavy metal pollutants
The use of an artificial neural network to improve precision in trace level, quantitative analysis of heavy metal pollutants
- Author(s): H.S. Manwaring
- DOI: 10.1049/cp:19950585
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
4th International Conference on Artificial Neural Networks — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): H.S. Manwaring Source: 4th International Conference on Artificial Neural Networks, 1995 p. 375 – 380
- Conference: 4th International Conference on Artificial Neural Networks
- DOI: 10.1049/cp:19950585
- ISBN: 0 85296 641 5
- Location: Cambridge, UK
- Conference date: 26-28 June 1995
- Format: PDF
The author has used various neural networks to process the response obtained from an electroanalytical technique used for the analysis of trace metal pollutants in liquids. A previous paper by H.S. Manwaring (1994), compared the capabilities of the GRNN and MLP in this respect. It is shown that using the neural network to make predictions of unknown sample concentrations shows an improvement, by a factor of about two, on the mean absolute error and the prediction confidence when compared with a traditional, calibration curve technique. In addition, the neural network method is shown to produce reliable predictions even with instrumental responses that are completely unsuitable for traditional processing.
Inspec keywords: water pollution measurement; computerised monitoring; neural nets; environmental science computing; chemical analysis
Subjects: Computerised instrumentation; Chemical variables control; Computerised instrumentation; Neural computing techniques; Pollution detection and control; Physics and chemistry computing; Environmental issues; Control engineering computing
Related content
content/conferences/10.1049/cp_19950585
pub_keyword,iet_inspecKeyword,pub_concept
6
6