Orthonormal Basis Lattice Neural Networks

被引:11
作者
Barmpoutis, Angelos [1 ]
Ritter, Gerhard X. [1 ]
机构
[1] Univ Florida, CISE Dept, Gainesville, FL 32611 USA
来源
2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2006年
关键词
D O I
10.1109/FUZZY.2006.1681733
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:331 / +
页数:2
相关论文
共 19 条
[1]  
ECCLES JC, 1977, UNDERSTANDING BRAIN
[2]   Some applications of morphological neural networks [J].
Graña, M ;
Raducanu, B .
IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, :2518-2523
[3]   Fuzzy lattice neurocomputing (FLN) models [J].
Kaburlasos, VG ;
Petridis, V .
NEURAL NETWORKS, 2000, 13 (10) :1145-1170
[4]   Clustering and classification in structured data domains using Fuzzy Lattice Neurocomputing (FLN) [J].
Petridis, V ;
Kaburlasos, VG .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2001, 13 (02) :245-260
[5]   Fuzzy lattice neural network (FLNN): A hybrid model for learning [J].
Petridis, V ;
Kaburlasos, VG .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1998, 9 (05) :877-890
[6]  
PETRIDIS V, 2001, P INT JOINT C NEUR N, V2, P1362
[7]  
Raducanu B, 2001, IEEE INT CONF ROBOT, P2059, DOI 10.1109/ROBOT.2001.932910
[8]  
RITTER G, 2006, P IEEE WORLD C COMP
[9]  
Ritter G. X., 1996, Proceedings of the 13th International Conference on Pattern Recognition, P709, DOI 10.1109/ICPR.1996.547657
[10]  
Ritter GX, 2004, IEEE IJCNN, P915