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A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition.

Chia-Feng Juang¹, Ksuan-Chun Ku

¹Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan, ROC.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|September 1, 2005

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A novel fuzzified Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (FTRFN) effectively processes fuzzy temporal data. This network learns rules online for applications like sequence prediction and gesture recognition.

Area of Science:

Artificial Intelligence
Fuzzy Systems
Machine Learning

Background:

Traditional recurrent fuzzy networks (TRFN) have limitations in handling fuzzy temporal signals.
Processing temporal data with uncertainty requires advanced fuzzy logic approaches.

Purpose of the Study:

To propose a fuzzified Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (FTRFN) for enhanced fuzzy temporal information processing.
To extend existing recurrent fuzzy network capabilities to handle fuzzy temporal signals represented by Gaussian or triangular fuzzy numbers.

Main Methods:

The FTRFN utilizes a similarity measure for matching input fuzzy variables with antecedent sets.
A TSK-type consequence involves linear combinations of fuzzy variables, with tunable coefficients for center and width.

Related Experiment Videos

Online learning constructs network rules concurrently, optimizing both structure and parameters.

Main Results:

The FTRFN successfully processed one-dimensional and two-dimensional fuzzy temporal sequence prediction.
The network demonstrated effectiveness in CCD-based temporal gesture recognition.
Performance validation confirmed the FTRFN's capability in diverse fuzzy temporal data applications.

Conclusions:

The proposed FTRFN offers a robust framework for fuzzy temporal information processing.
The network's online learning capability allows for adaptive rule construction.
FTRFN shows significant potential for various real-world applications involving fuzzy temporal data.