Backpropagation neural network model for detecting artificial emotions with color

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.