Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives most accurate results? Econometric can deal with different types of time series and good results, but specific cases, they may fail to provide predictions. Recently, new techniques borrowed from soft computing area were adopted for economic forecasting. S...

Abstract: Mukherjee and Basu (Application of fuzzy ranking method for solving assignment problems with fuzzy costs, International Journal of Computational and Applied Mathematics, 5, 2010, 359-368) proposed a new method for solving fuzzy assignment problems. In this paper, some fuzzy assignment problems and fuzzy travelling salesman problems are chosen which cannot be solved by using the fore-m...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Speciication languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the IS problem domain, may be diicult to model in crisp or precise terms. It may also be desirable that formal modelling...

Marketing deals with identifying and meeting the needs of customers. It is therefore both an art and a science. To bridge the gap between art and science, soft computing, or computing with words, could be an option. This chapter introduces fundamental concepts such as fuzzy sets, fuzzy logic, and computing with linguistic variables and terms. This set of fuzzy methods can be applied in marketin...

This paper deals with the simplest fuzzy PI controllers under various defuzzification methods. The various simplest fuzzy PI controllers consist of two triangular input fuzzy sets and three triangular output fuzzy sets on the universe of discourse of input and output variables, respectively, linear control rules, intersection T-norm, Lukasiewicz and Zadeh OR T-conorms, and any valid inference m...

anti-lock braking system (abs) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. for this reason an interval type-2 fuzzy controller is de...

Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The propos...

In this work the importance of fuzzy based clustering methods is highlighted and their applications in the field of chemoinformatics, and issues involved are reviewed. The various methods and approaches of fuzzy clustering are outlined. The issue of number of valid clusters in a dataset is also discussed. The hyper dimensional chemical datasets are traditionally been treated only with the help ...