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An exploratory study of multimodal interaction modeling based on neural computation

Science China(Information Sciences) 2016年第9期59卷 63-78页

作者： Lu LU Fei LYU Feng TIAN Yineng CHEN Guozhong DAI Hongan WANG Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences

Multimodal interaction serves an important role in human-computer interaction. In this paper we propose a multimodal interaction model based on the latest cognitive research findings. The proposed model combines two proven neural computations, and helps to reveal the enhancement or depression influence of multimodal presentation upon the corresponding interaction task performance. A set of experiments is designed and conducted within the constraints of the model, which demonstrates the observed performance enhancement and depression effects. Our exploration and the experimental results help to further solve the question about how tactile feedback signal contribute the multimodal interaction efficiency which could provide guidelines for designing the tactile feedback in multimodal interaction.

关键词： human-computer interaction multimodal integration interaction model touch-included interac tion cognition multisensory integration neural computation brain coding

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Grading learning for blind source separation

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Science in China(Series F) 2003年第1期46卷 31-44页

作者：张贤达朱孝龙保铮 Key Laboratory for Radar Signal Processing Xidian University Department of Automation State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China

By generalizing the learning rate parameter to a learning rate matrix, this paper proposes a grading learning algorithm for blind source separation. The whole learning process is divided into three stages: initial stage, capturing stage and tracking stage. In different stages, different learning rates are used for each output component, which is determined by its dependency on other output components. It is shown that the grading learning algorithm is equivariant and can keep the separating matrix from becoming singular. Simulations show that the proposed algorithm can achieve faster convergence, better steady-state performance and higher numerical robustness, as compared with the existing algorithms using fixed, time-descending and adaptive learning rates.

关键词： blind source separation independent component analysis neural computation adaptive learning.

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