Title :
Water mine data fusion and model recognition
Author :
Liu, Haibo ; Gu, Guochang ; Shen, Jing ; Fu, Yan
Author_Institution :
Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., China
fDate :
27 June-3 July 2005
Abstract :
It is significant for a MCS (mine countermeasure system) to recognize the model of a water mine exactly in order to take right destroying measures. In this paper, the ABNET proposed by L.N. de Castro is simplified and employed in a multi-agent-based MCS for fusing the feature data and recognizing the model of water mines. The simplified ABNET (sABNET) is a two-layer Boolean network which number of outputs is adaptable according to the task and which recognition precision can be controlled by the immune affinity threshold. Compared with Castro´s work, the sABNET converges more quickly.
Keywords :
electronic countermeasures; geophysics computing; image recognition; multi-agent systems; neural nets; sensor fusion; data fusion; immune affinity threshold; multi-agent-based mine countermeasure system; two-layer Boolean network; water mine model recognition; Artificial neural networks; Computer science; Data mining; Educational institutions; Immune system; Neural networks; Neurons; Object detection; Production systems; Shape;
Conference_Titel :
Information Acquisition, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9303-1
DOI :
10.1109/ICIA.2005.1635152
