Statistical Learning Theory is commonly regarded as a sound framework within which we handle a variety of learning problems in presence of small size data samples. It has become a rapidly progressing research area in machine learning. The theory is based on real random samples and as such is not ready to deal with the statistical learning problems involving complex fuzzy random samples, which w...

A new complex fuzzy computing paradigm using complex fuzzy sets (CFSs) to the problem of time series forecasting is proposed in this study. Distinctive from traditional type-1 fuzzy set, the membership for elements belong to a CFS is characterized in the unit disc of the complex plane. Based on the property of complex-valued membership, CFSs can be used to design a neural fuzzy system so that t...

In this paper, we introduce the concept of complex multi-fuzzy sets (CMkFSs) as a generalization of the concept of multi-fuzzy sets by adding the phase term to the definition of multi-fuzzy sets. In other words, we extend the range of multi-membership function from the interval [0,1] to unit circle in the complex plane. The novelty of CMkFSs lies in the ability of complex multimembership functi...

In this paper, we first use a fuzzy preference relation with a membership function representing preference degree forcomparing two interval-valued fuzzy numbers and then utilize a relative preference relation improved from the fuzzypreference relation to rank a set of interval-valued fuzzy numbers. Since the fuzzy preference relation is a total orderingrelation that satisfies reciprocal and tra...

The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjus...

Fuzzy technology became a very important controlling method in complex systems where traditional methods are unsuccessful. It was proved in [19] that fuzzy rule systems can be used as general approximators of any complex continuous systems. The key element of the approximation process is the construction of the corresponding fuzzy rule system that encapsulates the knowledge on the problem domai...

In recent times, intuitionistic fuzzy sets introduced by Atanassov has been one of the most powerful and flexible approaches for dealing with complex and uncertain situations of real world. In particular, the concept of divergence between intuitionistic fuzzy sets is important since it has applications in various areas such as image segmentation, decision making, medical diagnosis, pattern reco...

Common uses of fuzzy logic control today are found in relatively simple applications in products. These applications only scratch the surface of the potential of fuzzy logic control in complex mechatronic systems. In order to demonstrate the utility of fuzzy logic in complex device control, a fuzzy logic fly-by-wire system has been developed for a model airplane. The model airplane is a complex...