Emotions were proven to lead an important role in human intelligence. Intelligent agents’ research produced many emotional agents. Research on human psychology had long considered the notion of an emotion (e.g., happy) to be a matter of degree; however, most existing research on emotional intelligent agents treat emotions as a blackand-white matter. We are proposing a model called FLAME − Fuzzy...

In this chapter we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies—fuzzy systems and evolutionary algorithms—to automatically produce diagnostic systems. We present two hybrid approaches: (1) a fuzzy-genetic algorithm, and (2) Fuzzy CoCo, a novel cooperative coevolutionary approach to fuzzy modeling. Both methods produce systems exhibiting high classif...

Please cite this article in press as: Talei, A., et a Expert Systems with Applications (2010), doi:10.1 Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in ...

In this paper a new fuzzy modeling algorithm is proposed and a new inference schema is developed for fuzzy reasoning that suits to the proposed approach

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...

Recently, various papers have given consideration to how fuzzy sets might be useful in geometric computing and solid modelling. This paper reviews this previous work and related topics, and adds further observations on the use of fuzzy sets for inexact shape modelling. It is noted that many serious problems remain to be solved. The wide range of solid modelling applications requiring the genera...

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance to<b...

This article discusses a rule-based fuzzy model for the identiication of nonlinear MISO (multiple input, single output) systems. The diss cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the numm ber of rules and input variables involved respecc tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...

Data-driven fuzzy modeling has been used in a wide variety of applications. However, in fuzzy rule-based models acquired from numerical data, redundancy often exists in the form of redundant rules or similar fuzzy sets. This results in unnecessary structural complexity and decreases the interpretability of the system. In this paper, a rule-base self-extraction and simpli&cation method is propos...