The above paper presents a rather complicated mechanism allowing fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules. The purpose of this comment is to present that the FuzzyCLIPS address this problem in simpler way.

In this paper we introduce an algebraic fuzzy equation of degree n with fuzzy coefficients and crisp variable, and we present an iterative method to find the real roots of such equations, numerically. We present an algorithm to generate a sequence that can be converged to the root of an algebraic fuzzy equation. 2007 Elsevier Inc. All rights reserved. MSC: 12E12; 39B22; 65H05

A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can...

In existing fuzzy logic controllers (FLCs), input variables are mostly the error and the change-of-error regardless of complexity of controlled plants. Either control input u or the change of control input Deltau is commonly used as its output variable. A rule table is then constructed on a two-dimensional (2-D) space. This scheme naturally inherits from conventional proportional-derivative (PD...

Variable threshold concept lattice (VTCL) was proposed by Ma et al.(2006), which provide a new parameterized way to obtain formal concepts from data with fuzzy attributes. Axiomatic Fuzzy Set (AFS) algebras were proposed by Liu (1998), which are new semantic methodology relating to the fuzzy theory. In this paper, in order to explore the relationship between the AFS algebras and VTCL, three alg...

There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete n...

We compare two methods regarding the factorization problem of binary, triadic, real and fuzzy data, namely Hierarchical Classes Analysis and the formal concept analytical approach to Factor Analysis. Both methods search for the smallest set of hidden variables, called factors, to reduce the dimensionality of the attribute space which describes the objects without losing any information. First, ...

For the evaluation of results from remote sensing and high-resolution spatial models it is often necessary to assess the similarity of sets of maps. This paper describes a method to compare raster maps of categorical data. The method applies fuzzy set theory and involves both fuzziness of location and fuzziness of category. The fuzzy comparison yields a map, which specifies for each cell the de...

The traditional regression analysis is usually applied to homogeneous observations. However, there are several real situations where the observations are not homogeneous. In these cases, by utilizing the traditional regression, we have a loss of performance in fitting terms. Then, for improving the goodness of fit, it is more suitable to apply the so-called clusterwise regression analysis. The ...

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Theref...