—This paper presents a novel approach to the representation of type-1 and type-2 fuzzy sets utilising computational geometry. To achieve this our approach borrows ideas from the field of computational geometry and applies these techniques in the novel setting of fuzzy logic. We provide new algorithms for various operations on type-1 and type-2 fuzzy sets… (More)
The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained… (More)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract—Construction of interval type-2 fuzzy set models is the first step in the Perceptual Computer, an implementation of Computing with Words. The Interval Approach (IA) has been so far the only… (More)
Traditionally fuzzy logic has been grounded in crisp logic. This paper challenges this idea, instead relating fuzzy logic to computational geometry. A geometric representation of a fuzzy set is given along with the AND and OR operations for such sets. Comparisons between the two methods are made.
Wireless Sensor Networks (WSN) can produce decisions that are unreliable due to the large inherent uncertainties in the areas which they are deployed. It is vital for the applications where WSN's are deployed that accurate decisions can be made from the data produced. Fault detection is a vital pursuit, however it is a challenging task. In this paper we… (More)
Defuzzification of type-2 fuzzy sets is a com-putationally intense problem. This paper proposes a new approach for defuzzification of interval type-2 fuzzy sets. The collapsing method converts an interval type-2 fuzzy set into a representative embedded set (RES) which, being a type-1 set, can then be de-fuzzified straightforwardly. The novel Representative… (More)
For generalised type-2 fuzzy sets the defuzzification process has historically been slow and inefficient. This has hampered the development of type-2 Fuzzy Inferencing Systems for real applications and therefore no advantage has been taken of the ability of type-2 fuzzy sets to model higher levels of uncertainty. The research reported here provides a novel… (More)