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Two types of methods for rule-based fuzzy modeling are studied.
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.
In this paper, we present a new learning method for rule-based feed-forward and recurrent fuzzy systems.
A level-set method is employed as an alternative approach to the popular homogenization-based methods of rule of mixtures for multi-material modeling.
Current methods for association rule mining have shown unstable performance for different database types and under-utilize the benefits of multi-core shared memory machines.
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.
Then, the search results were filtered to identify the potential target mimics of certain miRNAs according to the rules established based on the previous experimental experiences [ 7- 9] (see Methods for detailed rule-based filtering).
The genetic fuzzy logic system in this study is proposed as a method for automatic rule generation in fuzzy systems for structural damage detection.
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