This paper discusses the generalization capability of neural networks based on various fuzzy operators introduced earlier by the authors as Fuzzy Flip-Flop based Neural Networks (FNNs), in comparison with standard (e.g. tansig function based, MATLAB Neural Network Toolbox type) networks in the frame of simple function approximation problems. Various fuzzy neurons, one of them based on a pair of...

This volume is about knowledge processing with interval and soft computing, i.e., about techniques that use both interval and soft computing to process knowledge and about the results of applying these techniques. To better understand these techniques, in Chapter 1, we described fundamentals of interval computing, and in Chapter 2, we described the fundamentals of soft computing. Now it is time...

A method based on fuzzy set theory is presented to incorporate imprecise thermodynamic parameters into chemical equilibrium calculations of aqueous systems. Imprecision may arise from uncertainties in experimental parameter determination as well as from inconsistency of available data in the literature. Fuzzy numbers with different shapes of membership functions are used to express imprecision ...

abstract drought is a natural creeping event that starts due to lower moisture compared to normal condition. this phenomenon impacts all aspects of human activities. however there is neither any detailed definition nor a general and proper index for drought monitoring. in this study, fuzzy logic has been applied to deal with inherent uncertainties of the real world data. we presented a fuzzy mo...

This paper proposes a framework for evaluating information technology investments, integrating value chain analysis with activity-based costing and fuzzy logic. The proposed method should be particularly useful for businesses in emerging economies, where an uncertain economic environment is often combined with a lack of dependable, historical accounting data. The framework is illustrated by mea...

Hybrid systems composed of AI approaches have shown quite remarkable results in diagnosis. Designing of such multi-method sytems generally bears some diiculties in nding a uniform representation of inputs and outputs of their subsystems. Since Fuzzy Logic, too, has proven high importance in Artiicial Intelligence, due to its adequate pseudoverbal representation of knowledge, it is well suited t...

Fuzzy sets and rough sets address two important, and mutually orthogonal, characteristics of imperfect data and knowledge: while the former allow that objects belong to a set or relation to a given degree, the latter provide approximations of concepts in the presence of incomplete information. In this chapter, we demonstrate how these notions can be combined into a hybrid theory that is able to...

Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable...

PCA and ICA are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this work, an improved version of PCA and ICA is proposed for feature extraction to classify the ischemic beats...

In this paper, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 Takagi-Sugeno-Kang (TSK) fuzzy model from input/output data. Then the type-2 fuzzy theory is used to expand the type-1 model to a type-2 model. A sensitivity analysis is used to ascertain how a type-1 TSK model output depends upon the pre-initialized parameters and determin...