Cited by 3Engineering Education and PedagogyExperimental Learning in EngineeringUndergraduate Neuroscience Education and Research

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

A Practical Approach to Introducing Electrical Undergraduates to Artificial Neural Networks

Halkiadis, I. S.·2009·International Journal of Electrical Engineering & Education

Abstract

Artificial intelligence techniques are increasingly being applied to a variety of problems in the field of electrical engineering, and consequently universities worldwide are paying more attention to training electrical engineers in applying neural networks (ANNs) in practice. As part of the above training, an introductory practical project on ANNs has been offered as an elective to the undergraduates in the author's department. The project introduces a single layer ANN model consisting of two neurons. The algorithm, the hardware implementation and the test procedure concerning one of the neurons, are described. Then the students apply the same techniques for the other neuron and combine the two circuits to form the final ANN hardware. The agreement of the results conducted by the theoretical models with those obtained by the constructed circuits, allow the students to become familiar with ANN hardware techniques and realise that they present a practical and efficient solution.

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Research works printed but not linked: 6

  1. Keyhani A., 1994, Proc. PSCE 2004, IEEE Power System Conference & Exploration, October 10–13, 2004, New York
  2. Bouthiba T., 2004, Int. J. Appl. Comput. Sci., 14, 69
  3. King D., 2002, Proc. 2002 IEEE Intl Conf. on Sensors, 12–14 June 2002, Florida, USA, 2, 1374
  4. Lehtonen J., 2005, Proc. 2005 IEEE Intl Conf. on Systems, Man & Cybernetics, 2979
  5. Curriculum 2001 Joint Task Force, Computing Curricula 2001 Computer Science: Final Report, December 15, 2001, Los Alamitos, CA, available at http://www.computer.org/portal/cms_docs_ieeecs/ieeecs/education/cc2001/cc2001.pdf.
  6. Russell, ‘Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory 4. Experiences’, in Proc. 2005 ASEE Annual Conference & Exposition, June 20–23, 2004, Salt Lake City, USA, available at http://soa.asee.org/paper/conference/paper_view.cfm?id=21336.

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Metadata

Authors
Halkiadis, I. S.
Affiliations
Department of Electrical Engineering, Technological Institution of Chalkida, Psahna, Greece
Corresponding author
Halkiadis, I. S.
Venue
International Journal of Electrical Engineering & Education
Published
2009-04
Volume
46
Issue
2
Pages
121-132
ISSN
  • 0020-7209 (ISSN-L)
  • 2050-4578
OpenAlex
W2313776762
Cited by
2
References
26
Citations by year
2023: 1 · 2022: 1
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Electrical Engineering Education (publishedVersion)
Topics
  • Social Sciences › Education › Engineering Education and Pedagogy
  • Social Sciences › Education › Experimental Learning in Engineering
  • Social Sciences › Education › Undergraduate Neuroscience Education and Research
Keywords
artificial neural networks, neural network hardware, single-layer neural network, two-neuron network, electrical engineering education

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CoChat. (2026). Citation check: A Practical Approach to Introducing Electrical Undergraduates to Artificial Neural Networks. https://app.cochat.ai/papers/10.7227/ijeee.46.2.1
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Halkiadis, I. S. (2009). A Practical Approach to Introducing Electrical Undergraduates to Artificial Neural Networks. International Journal of Electrical Engineering & Education. https://doi.org/10.7227/ijeee.46.2.1