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Your search keyword '"Pal, N.R."' showing total 5 results
Start Over Author "Pal, N.R." Publisher elsevier b.v.
5 results on '"Pal, N.R."'

1. A fuzzy rule based approach to cloud cover estimation

Author

Ghosh, A., Pal, N.R., and Das, J.

Subjects
*METEOROLOGICAL precipitation, *CLIMATOLOGY, *TELECOMMUNICATION satellites, *ATMOSPHERIC research
Abstract

Abstract: A fuzzy rule based cloud classification scheme is proposed to estimate the cloud cover from satellite imagery. METEOSAT-5 images are classified into three classes: cloudy, partially cloudy, and clear sky. Five features, which measure the temporal and spatial properties of visible (VIS) and infrared (IR) images of METEOSAT-5, are used for this. The proposed classifier finds out a few human understandable rules (fuzzy rules) using exploratory data analysis. A novel attribute of the system is that it analyzes the behavior of misclassifications during training (i.e., typical mistakes) to extract a few more rules which are augmented to the initial rule base to improve its performance. The scheme is tested on images other than the training image(s) and the performance is found to be quite satisfactory. A post-processing scheme is also developed, which utilizes experts'' knowledge to generate additional rules to account for coastal region, sunglint areas, and snow-covered Himalayan region. This improves the performance of the system further. Finally, the classification results are compared with multispectral threshold tests, surface synoptic observations, and total cloud cover (tcdc) of reanalysis data produced by National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR). The high accuracy achieved by the proposed method may be attributed to (1) better design philosophy of classifiers; (2) good choice for the feature vectors; (3) accurate labeling of training data; and (4) exploitation of experts'' knowledge. [Copyright &y& Elsevier]

Published
2006
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2. Uncertainties with Atanassov’s intuitionistic fuzzy sets: Fuzziness and lack of knowledge

Author

Pal, N.R., Bustince, H., Pagola, M., Mukherjee, U.K., Goswami, D.P., and Beliakov, G.

Subjects
*FUZZY sets, *INTUITIONISTIC mathematics, *UNCERTAINTY (Information theory), *AXIOMS, *MATHEMATICAL analysis, *INFORMATION theory, *SET theory
Abstract

Abstract: We review the existing measures of uncertainty (entropy) for Atanassov’s intuitionistic fuzzy sets (AIFSs). We demonstrate that the existing measures of uncertainty for AIFS cannot capture all facets of uncertainty associated with an AIFS. We point out and justify that there are at least two facets of uncertainty of an AIFS, one of which is related to fuzziness while the other is related to lack of knowledge or non-specificity. For each facet of uncertainty, we propose a separate set of axioms. Then for each of fuzziness and non-specificity we propose a generating family (class) of measures. Each family is illustrated with several examples. In this context we prove several interesting results about the measures of uncertainty. We prove some results that help us to construct new measures of uncertainty of both kinds. [Copyright &y& Elsevier]

Published
2013
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3. RID3: An ID3-like algorithm for real data.

Author

Pal, N.R. and Chakraborty, Sukumar

Subjects
*GENETIC algorithms
Abstract

Proposes the ID3-like tree-based classifier named RID3 as a simple but effective scheme for ranking of the features according to their discriminability between classes. Values of the features in pattern recognition; Proposed scheme; Results of feature ranking; Creation of the tree in predicting the class of any data point; Tree tuning using genetic algorithms. Drawback of the ID3-type.

Published
1997
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5. On averaging operators for Atanassov’s intuitionistic fuzzy sets

Author

Beliakov, G., Bustince, H., Goswami, D.P., Mukherjee, U.K., and Pal, N.R.

Subjects
*FUZZY sets, *INTUITIONISTIC mathematics, *TRIANGULAR norms, *AGGREGATION operators, *OPERATOR theory, *SET theory, *GENERALIZATION, *MATHEMATICAL analysis
Abstract

Abstract: Atanassov’s intuitionistic fuzzy set (AIFS) is a generalization of a fuzzy set. There are various averaging operators defined for AIFSs. These operators are not consistent with the limiting case of ordinary fuzzy sets, which is undesirable. We show how such averaging operators can be represented by using additive generators of the product triangular norm, which simplifies and extends the existing constructions. We provide two generalizations of the existing methods for other averaging operators. We relate operations on AIFS with operations on interval-valued fuzzy sets. Finally, we propose a new construction method based on the Łukasiewicz triangular norm, which is consistent with operations on ordinary fuzzy sets, and therefore is a true generalization of such operations. [ABSTRACT FROM AUTHOR]

Published
2011
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