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Backpropagation neural network model for detecting artificial emotions with color

机译:反向传播神经网络模型,用于检测带有颜色的人工情感

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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.
机译:如今,情感已成为人类行为线索的关键位置,因此,当智能系统旨在模拟或预测人类反应时,应将其包含在明智的模型中。本研究利用神经网络模型之一的反向传播建立情感检测机制。这项研究将Thayer的情绪模式,模糊认知图和颜色理论整合和操纵到反向传播神经网络模型中,用于创新的情绪检测系统。本文使用四个情感组中的100个数据来训练神经网络中的权重,并使用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有效地通过人类反馈颜色来估计情绪。对于进一步的研究,色彩不是人类行为的唯一线索,甚至比人类互动的所有因素还要多。

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