  Support vector machines (SVMs) and fuzzy rule systems are functionally
equivalent under some conditions. Therefore, the learning algorithms developed
in the field of support vector machines can be used to adapt the parameters of
fuzzy systems. Extracting fuzzy models from support vector machines has the
inherent advantage that the model does not need to determine the number of
rules in advance. However, after the support vector machine learning, the
complexity is usually high, and interpretability is also impaired. This paper
not only proposes a complete framework for extracting interpretable SVM-based
fuzzy modeling, but also provides optimization issues of the models.
Simulations examples are given to embody the idea of this paper.
