Structure identification of fuzzy model
Fuzzy Sets and Systems
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Now comes the time to defuzzify neuro-fuzzy models
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Affective computing
Possibility theory is not fully compositional!: a comment on a short note by H.J. Greenberg
Fuzzy Sets and Systems
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
Semantics in Visual Information Retrieval
IEEE MultiMedia
Toward computers that recognize and respond to user emotion
IBM Systems Journal
Fuzzy-GIST for emotion recognition in natural scene images
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
3D fuzzy GIST to analyze emotional features in movies
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Hi-index | 0.01 |
In this paper, we propose an autonomous emotion development system with incremental learning for interacting with human subjects, and autonomously understanding the emotional status of humans. For the understanding of human emotion the proposed system needs human-like visual senses perceiving natural scenes as stimuli. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L@?C@?H@? color and orientation information. Additionally, it can sense inputs that have no analog in human senses-reading brain signals in human subjects. We consider the electroencephalography (EEG) signals which are stimulated by natural stimuli to form the semantic emotional features as well. We develop a novel adaptive neuro-fuzzy inference system (ANFIS) based on an incremental learning algorithm to autonomously develop the capability of understanding complex emotions. The proposed incremental modified ANFIS needs only the newly arrived data to adjust the shape of Gaussian membership functions with full covariance matrix, to generate new membership functions or new rules for labeling emotion according to the characteristics of the new data. Utilizing the developmental process, the proposed system can autonomously develop the mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with human subjects.