Fuzzy methods

Fuzzy methods are suitable for approximate reasoning (Isukapalli Georgopoulos, 2001), especially for analysis of systems where uncertainty arises due to vagueness or fuzziness or 

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F. Ehrentreich, Fuzzy Methods in Chemistry, in Encyclopedia of Computational Chemistry, P.v.R. Schleyer,... 

Otto M, Stingeder G, Piplits K, Grasserbauer M, Heinrich M (1992) Comparison of depth profiles in SIMS by a fuzzy method. Mikrochim Acta 106 163... 

Other fuzzy methods, such as Fuzzy Calculus, also exist, but are at present less widely used than Fuzzy logic. 

DE Quadros, T.F.P., Koppe, J.C., Strieder, A.J., Costa, J.F.C.L. 2006. Minerai-Potentiai Mapping A Comparison of Weights-of-Evidence and Fuzzy Methods. Natural Resources Research, 15, 49-65. 

A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins Struct Funct Bioinformatics... 

This section provides an overview of common methods for quantitative uncertainty analysis of inputs to models and the associated impact on model outputs. Furthermore, consideration is given to methods for analysis of both variability and uncertainty. In practice, commonly used methods for quantification of variability, uncertainty or both are typically based on numerical simulation methods, such as Monte Carlo simulation or Latin hypercube sampling. However, there are other techniques that can be applied to the analysis of uncertainty, some of which are non-probabilistic. Examples of these are interval analysis and fuzzy methods. The latter are briefly reviewed. Since probabilistic methods are commonly used in practice, these methods receive more detailed treatment here. The use of quantitative methods for variability and uncertainty is consistent with, or informed by, the key hallmarks of data... 

The suggested fuzzy method for calculation of critical meanings of parameters of loading allows managers to choose the optimum decision and may be nsed in the fields of soil remediation, water reservoirs restoration or air pnrification. 

M. Otto, H. Bandemer, A fuzzy method for component identification and mixture evaluation in the ultraviolet spectral range. Anal. Chim. Acta 191 (1986) 193. 

For description of the uncertainty due to variability and measurement uncertainty, statistical methods are used. Vague circumstances are characterized by fuzzy methods (cf. Section 8.3). 

An important use of fuzzy methods for Data Mining is for classification. Associations between inputs and outputs are known in fuzzy systems as fuzzy associative memories or FAMs. A FAM system encodes a collection of compound rules that associate multiple input statements with multiple output statements We combine such multiple statements using logical operators such as conjunction, disjunction and negation. 

Schalon, C., Surgand, J. S., Kellenberger, E., and Rognan, D. 2008. A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins... 

In this fuzzy method, the general fuzzy rules are defined as below ... 

Interval and fuzzy methods in the evolution of the design process... 

In this paper, different importance weight for each objective was considered. Weight of each objective can be achieved by using the method of AHP matrix of paired comparisons (using Eq. 4). To solve the problem with weighted max-min fuzzy method, we first need to set membership functions of fuzzy constraints and goals. Then, the minimum and maximum of each membership function are obtained under the constraints (Amid et al. 2006) (Table 2). 

SEFIDVASH, F., ELBERN, A., The study of transients in the fluidized bed nuclear reactor by linkage and fuzzy methods, ICONE 3 (3 Int. Conf. on Nuclear Engineering Kyoto, Japan, April 23-27, 1995) ASME. 

Fuzzy methods are also used to enhance the learning capabilities or performance of a neural network. This can be done by using fuzzy rules to change the learning rate, or by creating a network that works with fuzzy inputs. 

Comparison of Depth Profiles in SIMS by a Fuzzy Method. 

If it is not possible to obtain complete data for operational risk analysis an expert knowledge should be used. Database created in this way can be considered as a basis for risk modelling by means of fuzzy methods. 

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