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Part of the book series: Studies in Computational Intelligence ((SCI,volume 67))

Summary. Lattice based neural networks are capable of resolving some difficult non-linear problems and have been successfully employed to solve real-world problems. In this chapter a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the other dendrites. We present experimental results that demonstrate superior learning performance of the new Orthonormal Basis Lattice Neural Network (OB-LNN) over SB-LNNs.

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© 2007 Springer-Verlag Berlin Heidelberg

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Barmpoutis, A., Ritter, G.X. (2007). Orthonormal Basis Lattice Neural Networks. In: Kaburlasos, V.G., Ritter, G.X. (eds) Computational Intelligence Based on Lattice Theory. Studies in Computational Intelligence, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72687-6_3

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  • DOI: https://doi.org/10.1007/978-3-540-72687-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72686-9

  • Online ISBN: 978-3-540-72687-6

  • eBook Packages: EngineeringEngineering (R0)

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