In this paper, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map. Our computing method consists of static and dynamic reasoning. In static reasoning, inferring on causal path of graph drives fuzzy linguistic value between any vertices and edges. With dynamic reasoning, system behaves as a dynamical system and convolution as automata property.
In this article, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map. Our model consists of set of vertices and edges with value to be linguistic variables which are constrained by linguistic lattice. We figure out relationship between numeric and linguistic domain for fuzzifying data. We also prove convergence theorem on learning method for linguistic cognitive map.
In this paper, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map (LCM). Our model consists of a set of vertices and edges whose values are linguistic variables that are constrained by a linguistic lattice.The algorithm for the system studied consists of fuzzification, reasoning, and defuzzification. Further, the total effect between any two vertices in the LCM is computed.
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