Information Sciences: an International Journal
Similarity-based perceptual reasoning for perceptual computing
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Perceptual reasoning for perceptual computing: a similarity-based approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Uncertainty degree and modeling of interval type-2 fuzzy sets: Definition, method and application
Computers & Mathematics with Applications
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words (CWW). We have proposed a CWW architecture for making subjective judgments, called a Perceptual Computer (Per- C). Because words mean different things to different people, the Per-C uses interval type-2 fuzzy sets (IT2 FSs). The encoder of the Per-C transforms words, in an application-dependent word- codebook, into IT2 FSs. The central element of the Per-C is the CWW engine, which maps IT2 FSs to IT2 FSs. Several CWW engines have appeared in the literature, e.g., fuzzy IF- THEN rules to perform inference and/or reasoning based on Mamdani or TSK models, linguistic weighted averages (LWAs) to aggregate linguistic data, and linguistic summarization to perform human friendly data mining. In this paper a new CWW engine Perceptual Reasoning (PR) is proposed. It also uses fuzzy IF-THEN rules; however, unlike a traditional Mamdani or TSK model, in which fired rules are combined using the union, or addition, or during the defuzzification process, in PR a LWA is used to combine the fired rules. We prove that the output IT2 FSs of PR can only look like the IT2 FSs in the application codebook. This is very important for CWW, because the last component of the Per-C is a decoder which converts the CWW output IT2 FS back into a word, e.g. a word whose IT2 FS is most similar to it. Index Terms-- Computing with words, perceptual reason- ing, perceptual computer, interval type-2 fuzzy sets, linguistic weighted average