DocumentCode :
2415498
Title :
Orthonormal Basis Lattice Neural Networks
Author :
Barmpoutis, Angelos ; Ritter, Gerhard X.
Author_Institution :
Univ. of Florida, Gainesville
fYear :
0
fDate :
0-0 0
Firstpage :
331
Lastpage :
336
Abstract :
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 paper 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.
Keywords :
learning (artificial intelligence); neural nets; dendritic computing; nonlinear problem; orthonormal basis lattice neural network; superior learning performance; Artificial neural networks; Biological neural networks; Biological system modeling; Biology computing; Brain modeling; Computer networks; Fuzzy logic; Lattices; Neural networks; Neurons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
Type :
conf
DOI :
10.1109/FUZZY.2006.1681733
Filename :
1681733
Link To Document :
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