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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
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)