Artificial neuro fuzzy logic system for detecting human emotions

This paper presents an adaptive neuro/fuzzy system which can be trained to detect the current human emotions from a set of measured responses. Six models are built using different types of input/output membership functions and trained by different kinds of input arrays. The models are compared based on their ability to train with lowest error values. Many factors impact the error values such as input/output membership functions, the training data arrays, and the number of epochs required to train the model.

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