Techniques for Threat Assessment Based on Adaptive Intuitionistic Fuzzy Reasoning

Lei Ying-jie; Wang Bao-shu; Lu Yan-li

doi:10.3724/SP.J.1146.2006.00708

Volume 29 Issue 12

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(12): 2805-2809

Lei Ying-jie, Wang Bao-shu, Lu Yan-li . Techniques for Threat Assessment Based on Adaptive Intuitionistic Fuzzy Reasoning[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2805-2809. doi: 10.3724/SP.J.1146.2006.00708

Citation:

Lei Ying-jie, Wang Bao-shu, Lu Yan-li . Techniques for Threat Assessment Based on Adaptive Intuitionistic Fuzzy Reasoning[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2805-2809. doi: 10.3724/SP.J.1146.2006.00708

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Techniques for Threat Assessment Based on Adaptive Intuitionistic Fuzzy Reasoning

doi: 10.3724/SP.J.1146.2006.00708

Lei Ying-jie^{①②;王宝树},
Wang Bao-shu,
Lu Yan-li

Received Date: 2006-05-12
Rev Recd Date: 2006-10-26
Publish Date: 2007-12-19

Abstract

Abstract

To the issues of threat assessment (TA), a technique for TA based on Adaptive Neuro-Intuitionistic Fuzzy Inference System (ANIFIS) is proposed with intuitionistic fuzzy set theory introduced into the area of information fusion. First, the properties and vulnerabilities of the existing TA methods are analyzed. A model for TA on ANIFIS with Takagi-Sugeno type is established. Then, the attribute functions, i.e. membership and nonmembership functions, and the inference rules of the system variables are devised with computational relations between layers of input and output and a synthesized computational expression of system outputs ascertained. Subsequently, an analysis of global approximation property of the model is performed with a learning algorithm of neural net devised. Finally, the validity of the technique is checked and rationality of constructed model is verified by providing TA instances with 20 typical targets. The simulated results show that this method can enhance creditability of TA and improve quality of assessment with precision of synthetic values in reasoning output.

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References

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