计算机应用 ›› 2011, Vol. 31 ›› Issue (10): 2814-2817.DOI: 10.3724/SP.J.1087.2011.02814

• 人工智能 • 上一篇    下一篇

模拟退火免疫粒子群算法在皮肤电信号情感识别中的应用

周钰婷1,刘光远1,赖祥伟2   

  1. 1.西南大学 电子信息工程学院,重庆 400715
    2.西南大学 计算机与信息科学学院,重庆 400715
  • 收稿日期:2011-04-21 修回日期:2011-06-20 发布日期:2011-10-11 出版日期:2011-10-01
  • 通讯作者: 刘光远
  • 作者简介:周钰婷(1984-),女,山东莱芜人,硕士研究生,主要研究方向:智能信息处理、情感计算;刘光远(1961-),男,重庆人,教授,博士生导师,博士,主要研究方向:计算智能、情感计算;赖祥伟(1978-),男,四川简阳人,副教授,博士,主要研究方向:软件测试、情感计算。
  • 基金资助:

    国家自然科学基金资助项目(60873143);国家重点学科基础心理学科研基金资助项目(NKSF07003);中央高校基本科研业务费专项资金资助项目(XDJK2009B008)

Applications of simulated annealing-immune particle swarm optimization in emotion recognition of galvanic skin response signal

ZHOU Yu-ting1, LIU Guang-yuan1, LAI Xiang-wei2   

  1. 1.School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
    2.School of Computer and Information Science, Southwest University, Chongqing 400715, China
  • Received:2011-04-21 Revised:2011-06-20 Online:2011-10-11 Published:2011-10-01
  • Supported by:

    ;the National Key Subject Foundation for Basic Psychology

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摘要： 为了增强情感识别过程中皮肤电反应(GSR)信号特征选择的有效性,提出了一种改进的模拟退火免疫粒子群算法。首先,对342组被试6种情感的GSR信号进行去噪处理和原始特征提取;然后,将模拟退火机制引入到免疫粒子群(IPSO)算法的粒子更新过程中,使用新构造的模拟退火免疫粒子群(SA-IPSO)算法进行特征优化选择。实验表明:与IPSO相比,SA-IPSO能以较少特征获得较高的识别率,模拟退火机制的应用能更好地优化特征选择过程,且新的算法具有良好的全局收敛性能。

关键词: 情感识别, 皮肤电反应, 模拟退火机制, 免疫粒子群, 特征选择

Abstract: An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR). Firstly, 342 groups of GSR signal with each containing 6 kinds of affective data were denoised, and afterwards the original features were extracted. Then simulated annealing mechanism was introduced to the particle update process of Immune Particle Swarm Optimization (IPSO), and the Simulated Annealing-Immune Particle Swarm Optimization (SA-IPSO) was adopted for feature selection. The experimental results show that compared with IPSO, SA-IPSO can achieve relatively high recognition rate with fewer features, the application of simulated annealing mechanism can help with the optimization of feature selection, and the improved algorithm also performs well on global convergence.

Key words: emotion recognition, Galvanic Skin Response (GSR), simulated annealing mechanism, Immune Particle Swarm Optimization (IPSO), feature selection

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