Neural models for ambient temperature modelling

F. Ceravolo, B. Di Pietra, S. Pizzuti, G. Puglisi

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

In this work we show how to model ambient temperature through neural models. In particular we tried feed forward and fully recurrent architectures, trained with the back-propagation and evolutionary algorithms, to estimate the monthly average temperature and compared the results to the nearest neighbor approach. Therefore, the best neural model has been tested to get hourly estimations. We compared the outcomes to a well known tool which doesn't have such an estimation capability and results show that the proposed approach clearly outperforms the traditional ones. ©2008 IEEE.
Original languageEnglish
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, IEEE CIMSA 2008 - , Turkey
Duration: 1 Jan 2008 → …

Conference

Conference2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, IEEE CIMSA 2008
CountryTurkey
Period1/1/08 → …

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Control and Systems Engineering

Cite this

Ceravolo, F., Di Pietra, B., Pizzuti, S., & Puglisi, G. (2008). Neural models for ambient temperature modelling. Paper presented at 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, IEEE CIMSA 2008, Turkey. https://doi.org/10.1109/CIMSA.2008.4595833