TASK SEGMENTATION IN A MOBILE ROBOT BY MNSOM AND CLUSTERING WITH SPATIO-TEMPORAL CONTIGUITY

被引:0
作者
Muslim, Muhammad Aziz [1 ]
Ishikawa, Masumi [2 ]
Furukawa, Tetsuo [2 ]
机构
[1] Univ Brawijaya, Dept Elect Engn, Malang 65145, Indonesia
[2] Kyushu Inst Technol, Dept Brain Sci & Engn, Kitakyushu, Fukuoka 8080196, Japan
来源
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2009年 / 5卷 / 04期
关键词
mnSOM; Task segmentation; Clustering; Mobile robot; Temporal contiguity; Spatio-temporal contiguity;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In our previous study, task segmentation was done by mnSOM, using prior information that winner modules corresponding to subsequences in the same class share the sane label. Since. this prior information is not available in real situation, segmentation. thus obtained should be regarded as the upper bound for the performance, not as a candidate for performance comparison. Present paper proposes to do task segmentation by applying various clustering methods to the resulting mnSOM, without using the above prior information. Firstly, we use the conventional hierarchical clustering. It assumes that. the distances between any pair of modules are provided with precision, but this is not the case in mnSOM. Secondly, we used a clustering method based on only the distance between spatially adjacent, modules with modification by their temporal contiguity. In the robotic field 1, the segmentation performance by the hierarchical clustering is very close to the upper bound for novel data. In the robotic field 2, the segmentation performance by clustering with the spatio-temporal contiguity is very close to the upper bound for novel data. Therefore, the proposed methods demonstrated their effectiveness in segmentation.
引用
收藏
页码:865 / 875
页数:11
相关论文
共 12 条
[1]  
Duda R. O., 2000, Pattern classification
[2]  
FURUKAWA T, 2004, P 2004 INT S NONL TH, P231
[3]   Adaptive Mixtures of Local Experts [J].
Jacobs, Robert A. ;
Jordan, Michael I. ;
Nowlan, Steven J. ;
Hinton, Geoffrey E. .
NEURAL COMPUTATION, 1991, 3 (01) :79-87
[4]   SELF-ORGANIZED FORMATION OF TOPOLOGICALLY CORRECT FEATURE MAPS [J].
KOHONEN, T .
BIOLOGICAL CYBERNETICS, 1982, 43 (01) :59-69
[5]  
Kohonen T., 1995, Self-organizing maps
[6]  
Muslim MA, 2007, NEURAL COMPUT APPL, V16, P571
[7]  
MUSLIM MA, 2007, INT WORKSH SELF ORG
[8]  
MUSLIM MA, 2006, P 2006 IEEE WORLD C, P6542
[9]   Annealed competition of experts for a segmentation and classification of switching dynamics [J].
Pawelzik, K ;
Kohlmorgen, J ;
Muller, KR .
NEURAL COMPUTATION, 1996, 8 (02) :340-356
[10]   Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems [J].
Tani, J ;
Nolfi, S .
NEURAL NETWORKS, 1999, 12 (7-8) :1131-1141