Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition

Xiao-dong YU; Ying-jie LEI; Ya-fei SONG; Shao-hua YUE; Jun-hong HU

doi:10.11959/j.issn.1000-436x.2015260

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Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition

academic paper | 更新时间：2024-06-05

- Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition
- Journal on Communications Vol. 36, Issue 10, Pages: 165-171(2015)
- 作者机构：
  
  1. 空军工程大学防空反导学院，陕西西安 710051
  2. 中国人民解放军 94936部队，浙江杭州 310021
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61272011);The National Natural Science Foundation of China(61309022);The National Natural Science Foundation of China(61402517)
- DOI：10.11959/j.issn.1000-436x.2015260
  CLC： TP182;TP391
- Online First：2015-10，
  
  Published：25 October 2015
- 稿件说明：
移动端阅览
Xiao-dong YU, Ying-jie LEI, Ya-fei SONG, et al. Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition[J]. Journal on Communications, 2015, 36(10): 165-171.
DOI：

Xiao-dong YU, Ying-jie LEI, Ya-fei SONG, et al. Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition[J]. Journal on Communications, 2015, 36(10): 165-171. DOI： 10.11959/j.issn.1000-436x.2015260.

摘要

针对现有直觉模糊核匹配追踪算法采用部分样本进行训练和停机策略而导致学习机泛化能力下降的缺陷，结合集成学习的思想，提出了一种基于直觉模糊核匹配追踪集成的目标识别方法。该算法通过采用样本扰动和参数扰动的二重扰动策略产生子学习机，并利用多数投票法对其识别结果进行融合，从而提高了集成学习机的分类精度和泛化性能。实验结果表明，与传统方法相比，该方法可获得更优的识别效果，有效提高了识别精度，并能避免采样学习带来的不稳定性。

Abstract

Considering that the generalization of the learning machine performed poorly in the present intuitionistic fuzzy kernel matching pursuit algorithm(IFKMP)due to its training method and stopping criteria

a new recognition method based on intuitionistic fuzzy kernel matching pursuit ensemble(IFKMPE)was proposed by introducing the idea of ensemble learning.In IFKMPE

the double perturbation strategy including sample and parameter perturbation was applied to generate the sub-learning machine

the recognition results were fused by the principle of majority voting

and therefore both the classify accuracy and generation ability were enhanced.Simulation results show the new algorithm IFKMPE performs better in terms of recognition accuracy and stability of sample learning compared with the traditional ones.

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references

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