In the context of culture-aware robotics, we propose a method for the explicit, on-line mapping between cultural variables and robot behaviour parameters which relies on the linguistic variable formalism, fuzzy clustering and the principles of fuzzy controllers. As a case study, we consider the adaptation of the Human-Robot conversational distance to Hofstede’s cultural dimension of Individualism.

Multidimensional association rule mining searches for interesting relationship among the values from different dimensions/attributes in a relational database. In this method the correlation is among set of dimensions i.e., the items forming a rule come from different dimensions. Therefore each dimension should be partitioned at the fuzzy set level. This paper proposes a new algorithm for genera...

This paper proposes a new type of a support vector machine which uses a kernel constituted from fuzzy basis functions. The proposed network combines the characteristics both of a support vector machine and a fuzzy system: high generalization performance, even when the dimension of the input space is very high, structured and numerical representation of knowledge and ability to extract linguisti...

The evolutionary learning of fuzzy neural networks (FNN) consists of structure learning to determine the proper number of fuzzy rules and parameters learning to adjust the network parameters. Many optimization algorithms can be applied to evolve FNN. However the search space of most algorithms has fixed dimension, which cannot suit to dynamic structure learning of FNN. We propose a novel techni...

In this study, we present a BCC detector based on confocal Raman spectroscopy using maximum a posteriori probability (MAP) and fuzzy algorithm. The preprocessing step consists of data normalization with minmax method and dimension reduction with principal components analysis (PCA). To enhance the classification performance, we divide Raman spectra into two significant protein bands, i.e., Amid ...

this paper analyzes a linear system of equations when the righthandside is a fuzzy vector and the coefficient matrix is a crisp m-matrix. thefuzzy linear system (fls) is converted to the equivalent crisp system withcoefficient matrix of dimension 2n × 2n. however, solving this crisp system isdifficult for large n because of dimensionality problems . it is shown that thisdifficulty may be avoide...

In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm’s performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum to [Int. J. Approx. Reason. 32 (2003)...

Fuzzy set theory is applied to quantify the non-probabilistic uncertainty alternatively termed as epistemic uncertainty. An algorithm “Fuzzy Centered Radius” has been developed for quantification of epistemic uncertainty. Uncertainty analysis is also carried out using fuzzy vertex method. Analytical solution of one dimension heat conduction and numerical solution of heat removal from circular f...

Fuzzy extractors are a powerful tool to extract randomness from noisy data. A fuzzy extractor can extract randomness only if the source data is discrete while in practice source data is continuous. Using quantizers to transform continuous data into discrete data is a commonly used solution. However, as far as we know no study has been made of the effect of the quantization strategy on the perfo...

Analytical analysis of the structure of fuzzy controllers is important as it provides insightful information and makes it possible to eliminate or reduce the trial-and-error effort in system design. Most results available in the literature only deal with the fuzzy controllers using up to three input variables (i.e. PI, PD, or PID type with the input space being three dimensional at most). In th...