Backpropagation neural network model for detecting artificial emotions with color
Backpropagation neural network model for detecting artificial emotions with color
复制标题
用颜色检测人工情绪的反向传播神经网络模型
DOI：
10.1109/icawst.2013.6765479
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Guey
中科院分区：
文献类型：
--
作者：
Min;Guey
如今，情感被引入到人类行为线索的关键位置，因此当一个智能系统旨在模拟或预测人类的反应时，它应该被包括在敏感模型中。本研究利用神经网络模型中的BP神经网络建立情绪检测机制。本研究将塞耶的情绪模型、模糊认知图和颜色理论集成到反向传播神经网络模型中，构建了一种新颖的情绪检测系统。本文使用四个情感组的100个数据来训练神经网络中的权值，并用300个数据来验证该系统的准确性。结果表明，反向传播神经网络可以根据人的反馈颜色对情绪进行有效的估计。对于进一步的研究，色彩不会是人类行为的唯一线索，甚至超过了所有来自人类互动的因素。
Nowadays, emotion is leaded into a key position of human behavior clue, and hence it should be included within the sensible model when an intelligent system aims to simulate or forecast human responses. This research utilizes backpropagation one of neural network model to build the emotion detecting mechanism. This research integrates and manipulates the Thayer's emotion mode, Fuzzy Cognitive Maps and color theory into the backpropagation neural network model for an innovative emotion detecting system. This paper uses 100 data in four emotion groups to train the weight in the neural network and use 300 data to verify the accuracy in this system. The result reveals that backpropagation neural network can be effective estimation the emotion by feedback color from human. For the further research, colors will not the only human behavior clues, even more than all the factors from human interaction.