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In this paper, we describe a paradigm for learning fuzzy rules using genetic algorithms (GA). We formulate our problem of learning as follows: given a set of linguistic values that characterize the input and output state variables of the system in consideration, derive an n-rule fuzzy control algorithm. The value n represents a specified constraint of the GA in searching for a functional ruleset...
In this paper, genetic algorithms are used in the study to maximise the performance of a fuzzy logic controller through the search of a subset of rule from a given knowledge base to achieve the goal of minimising the number of rules required. Comparisons are made between systems utilising reduced rules and original rules to verify the outputs. As an example of non-linear system, an inverted pendulum...
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