In Section 3. 5 we introduce two techniques for training fuzzy systems based on clustering. The first uses"c-means clustering" and least squares to train the premises and consequents, respectively, of the Takagi-Sugeno fuzzy system; while the second uses a nearest neighborhood technique to train standard fuzzy systems. In Section 3. 6 we present two learning from examples"(LFE)methods for constructing rules for fuzzy systems from input output data. Compared to the previous methods these do not use optimization to construct the fuzzy system parameters. Instead, the lfe methods are based on simple procedures to extract rules directly from the data11 ◼ In Section 3.5 we introduce two techniques for training fuzzy systems based on clustering. The first uses "c-means clustering" and least squares to train the premises and consequents, respectively, of the Takagi-Sugeno fuzzy system; while the second uses a nearest neighborhood technique to train standard fuzzy systems. In Section 3.6 we present two "learning from examples" (LFE) methods for constructing rules for fuzzy systems from input￾output data. Compared to the previous methods, these do not use optimization to construct the fuzzy system parameters. Instead, the LFE methods are based on simple procedures to extract rules directly from the data