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Quantitative object motion prediction by an ART2 and Madaline combined neural network

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Abstract

An ART2 and a Madaline combined neural network is applied to predicting object motions in dynamic environments. The ART2 network extracts a set of coherent patterns of the object motion by its self-organizing and unsupervised learning features. The identified patterns are directed to the Madaline network to generate a quantitative prediction of the future motion states. The method does not require any presumption of the mathematical models, and is applicable to a variety of situations.

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Authors and Affiliations

  1. Digital Imaging and Computer Vision Laboratory, University of Nebraska at Omaha, NE 68182-0050, Omaha, USA

    Qiuming Zhu

  2. Department of Computer Science, University of Saskatchewan, Sask. S7N 0W0, Saskatoon, Canada

    Ahmed Y. Tawfik

Authors
  1. Qiuming Zhu
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  2. Ahmed Y. Tawfik
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Zhu, Q., Tawfik, A.Y. Quantitative object motion prediction by an ART2 and Madaline combined neural network. Neural Process Lett 2, 19–21 (1995). https://doi.org/10.1007/BF02312378

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  • DOI: https://doi.org/10.1007/BF02312378

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