Architectures and application of data fusion system based on neural system evolution

Zhonghua Fang; Tao Chen; Baichen Zhai; Yan Zhao; Zhenjiang Tan

doi:10.1117/12.451008

6 December 2002 Architectures and application of data fusion system based on neural system evolution

Zhonghua Fang, Tao Chen, Baichen Zhai, Yan Zhao, Zhenjiang Tan

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.451008
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

An evolution model of data fusion system based on evolution procedure of nervous system is proposed. There are lots of similar characteristics between the evolution of nervous system and the development of data fusion system. It is reasonable to try to find guidelines in the theory of nervous system. For example, the function and structure of data fusion nodes almost take a same role as neurons in nervous system do, so we name the data fusion nodes as data fusion units. Just as the nervous system, the basic evolution architectures of data fusion system include four phases: Chaos (Autonomous), Fully Distributed, Centralized, and Internal Model Based Hierarchical. In the last phase of evolution, interface among unites become independent intelligent parts step by step. It provides a more flexible hierarchical data fusion architecture, which makes it be possible to simulate the regulation and adaptation mechanism of nervous system. The application analyses of these mechanisms to the data fusion systems proved that this dynamic hierarchy architecture is capable of deciding not only what to fuse and how to fuse but also when to fuse.

Citation Download Citation

Zhonghua Fang, Tao Chen, Baichen Zhai, Yan Zhao, and Zhenjiang Tan "Architectures and application of data fusion system based on neural system evolution", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.451008

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KEYWORDS

Data fusion

Nervous system

Data modeling

Neurons

Sensors

Field emission displays

Databases

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