A prototype-based rule inference system incorporating linear functions
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Cited by (12)
Anchor concept: A conceptual model with an expandable boundary
2016, Knowledge-Based SystemsCitation Excerpt :Gardenfors [10] has introduced the conceptual spaces theory, concepts are described as convex regions of a conceptual space, which provides a natural relationship to prototype theory. Lawry [11–13] and Tang [14,15] have introduced another prototype theory approach for vague concepts modeling, in order to define boundaries for labels and measure the vagueness of concept, uncertain thresholds is adopted on the distance between objects and prototypes, which provides a relationship between the epistemic view of vagueness and prototype theory. These works focus on measure of conceptual distance (or similarity), which is an important fundamental aspect of prototype theory approach.
A bipolar model of vague concepts based on random set and prototype theory
2012, International Journal of Approximate ReasoningSignatures: Definitions, operators and applications to fuzzy modelling
2012, Fuzzy Sets and SystemsLVQ algorithm with instance weighting for generation of prototype-based rules
2011, Neural NetworksCitation Excerpt :Despite these arguments prototype-based rules are still much less popular than other forms of rules. The use of prototype-based rule systems with linear functions for control (Tang & Lawry, 2010) may use linguistic labels but it is simply a basis set expansion technique that does not lead to comprehensible description of data. Similarity-Based Methods offer a rich framework (Duch, 2000; Duch, Adamczak, & Diercksen, 2000) for construction of such methods.
Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function
2015, Journal of Intelligent and Fuzzy SystemsOn the development of signatures for Artificial Intelligence applications
2014, IEEE International Conference on Fuzzy Systems