Until now single values of IRR are traditionally used to estimate the time value of cash flows. Since uncertainty exists in estimating cost data, the resulting decision may not be reliable. The most commonly cited drawbacks to using the internal rate of return in evaluatton of deterministic cash flow streams is the possibility of multiple conflicting internal rates of return. In this paper we p...

The aim of this paper is to address an emergent and important area of research, fuzzy control, by means of an inductive reasoning approach derived from the General System Theory, called Fuzzy Inductive Reasoning (FIR). This eld has had a growing interest and development in the last years, and has proved to be very useful for modeling and prediction of a large variety of systems from diierent ar...

Yager’s ordered weighted averaging (OWA) operator has been widely used in soft decision making to aggregate experts’ individual opinions or preferences for achieving an overall decision. The traditional Yager’s OWA operator focuses exclusively on the aggregation of crisp numbers. However, human experts usually tend to express their opinions or preferences in a very natural way via linguistic te...

In this paper, we proposed a new form of type-2 fuzzy data points(T2FDPs) that is normal type-2 data points(NT2FDPs). These brand-new forms of data were defined by using the definition of normal type-2 triangular fuzzy number(NT2TFN). Then, we applied fuzzification(alpha-cut) and type-reduction processes towards NT2FDPs after they had been redefined based on the situation of NT2FDPs. Furthermor...

A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-SugenoKang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to m...

This paper discusses a general approach to qualitative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, a...

The Unified Modeling Language has become a widespread notation for conceptual modeling, and it currently provides a number of extensibility mechanisms to tailor it to specialized modeling issues. Some extensions have been proposed to extend the UML for fuzzy modeling, but without explicitly considering strict additivity and semantic compatibility with the original specification. In this paper, ...

Abstract— Studies on the effectiveness of fuzzy logic for nonlinear modeling are presented. Although successful applications of fuzzy logic in many diverse areas are reported in the literature, commonly the explicit description of the key features of fuzzy logic yielding these outcomes is not addressed. The present research addresses this issue, to understand the basic features of fuzzy logic i...

Abstract. The diagnostic decision in medicine is frequently encountered with uncertainties. Modeling of uncertainties in the process of diagnosis of disease under fuzzy environment is an important subject. Various efforts have been made to model the uncertainties in this area through fuzzy sets and its generalizations. The theory of type-2 fuzzy sets is an intuitive and computationally feasible...

The fuzzy modeling technology, which uses a rule-based description of the relationship between variables, is discussed. We then turn to the problem of the simultaneous solution of relationships involving fuzzy models. We illustrate this problem by considering the equilibrium problem in economics. In this case we have two relationships, one between price and supply and the other between price an...