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An Incremental Probabilistic Neural Network for Regression and Reinforcement Learning Tasks

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Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6353))

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Abstract

This paper presents a new probabilistic neural network model, called IPNN (for Incremental Probabilistic Neural Network), which is able to learn continuously probability distributions from data flows. The proposed model is inspired by the Specht’s general regression neural network, but have several improvements which makes it more suitable to be used on-line in and robotic tasks. Moreover, IPNN is able to automatically define the network structure in an incremental way, with new units added whenever necessary to represent new training data. The performed experiments shows that IPNN is very useful in regression and reinforcement learning tasks.

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Author information

Authors and Affiliations

  1. UFRGS – Informatics Institute Porto Alegre, CEP 91501-970, RS, Brazil

    Milton Roberto Heinen & Paulo Martins Engel

Authors
  1. Milton Roberto Heinen
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  2. Paulo Martins Engel
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Editor information

Editors and Affiliations

  1. Department of Informatics, TEI of Thessaloniki, 57400, Sindos, Greece

    Konstantinos Diamantaras

  2. School of Physics, Astronomy, and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Wlodek Duch

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200, Orestiada Thrace, Greece

    Lazaros S. Iliadis

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Heinen, M.R., Engel, P.M. (2010). An Incremental Probabilistic Neural Network for Regression and Reinforcement Learning Tasks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_22

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  • DOI: https://doi.org/10.1007/978-3-642-15822-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15821-6

  • Online ISBN: 978-3-642-15822-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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