Journal of Systems Engineering and Electronics ›› 2006, Vol. 17 ›› Issue (3): 495-501.doi: 10.1016/S1004-4132(06)60085-6

• ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning

Zhang Yinan1, Sun Qingwei2, Quan He1, Jin Yonggao3 & Quan Tax fan1

  1. 1. Dept. of Electronics and Communication Engineering, Harbin Inst. of Technology, Harbin 150001, P. R. China;
    2. Mobile Communications Corporation of China, Harbin 150001, P. R China;
    3. Dept. of Electronics and Information Engineering of Yanbian Univ. , Yanbian 617100, P. R. China
  • Online:2006-09-25 Published:2006-09-25


In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusioa Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural
networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference
knowledge base and weighted fusion sectioa The simulation result demonstrates that the proposed fusion model
and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the
conventional Kaiman weighted fusion algorithm.

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