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The subsequent section describes the method for rule base and input values transmission to the FPGA board.
The next section briefly describes the basics of the entire system architecture followed by which the method for rule base extraction is proposed.
Then, the rule consequent is correlated with the strength value of the rule antecedent; the most common method for rule implication is to cut the consequent membership function at the level of the antecedent truth.
PART is an indirect method for rule generation.
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In this paper, we present a new learning method for rule-based feed-forward and recurrent fuzzy systems.
Firstly, when different SNP sets were compared, our results did not substantiate previous results that using more SNPs yielded better results; instead, our results indicated that the best SNP set may depend on the actual method used for rule construction.
Two types of methods for rule-based fuzzy modeling are studied.
The genetic fuzzy logic system in this study is proposed as a method for automatic rule generation in fuzzy systems for structural damage detection.
They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules.
This paper presents a design method for fuzzy rule-based systems that performs data modeling consistently according to the symbolic relations expressed by the rules.
We present a new method for learning rules from numerical data by using a combination of geometric and graph-theoretic methods.
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