The Experts below are selected from a list of 1097562 Experts worldwide ranked by ideXlab platform
Ibrahim Ozkan - One of the best experts on this subject based on the ideXlab platform.
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minimax e stable cluster validity index for type 2 Fuzziness
Information Sciences, 2012Co-Authors: Ibrahim Ozkan, Burhan I TurksenAbstract:In this paper, we concentrate on the usage of uncertainty associated with the level of Fuzziness in determination of the number of clusters in FCM for any data set. We propose a MiniMax @e-stable cluster validity index based on the uncertainty associated with the level of Fuzziness within the framework of interval valued Type 2 Fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of Fuzziness. Upper and lower values of the level of Fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as m=2.6 and 1.4, respectively, in our previous studies. Our investigation shows that the stability of cluster centers with respect to the level of Fuzziness is sufficient for the determination of the number of clusters.
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minimax e stable cluster validity index for type 2 Fuzziness
North American Fuzzy Information Processing Society, 2010Co-Authors: Ibrahim Ozkan, Burhan I TurksenAbstract:Uncertainty is a central part of many data analysis methodologies. Although quantifying the uncertainty has long been discussed, the research on it is still in progress. The level of Fuzziness in fuzzy system modeling is a source of uncertainty which can be classified as a parameter uncertainty. Upper and lower values of the level of Fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as 2.6 and 1.4 respectively in our previous studies. In this paper, we concentrate on the usage of uncertainty associated with the level of Fuzziness in determination of the number of clusters in FCM in any data. We propose MiniMax e-stable cluster validity index based on the uncertainty associated with the level of Fuzziness within the framework of Interval Valued Type 2 Fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of Fuzziness. Our investigation shows that the half range of upper and lower levels of Fuzziness would be enough to determine the optimum number of clusters.
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upper and lower values for the level of Fuzziness in fcm
Information Sciences, 2007Co-Authors: Ibrahim Ozkan, I B TurksenAbstract:The level of Fuzziness is a parameter in fuzzy system modeling which is a source of uncertainty. In order to explore the effect of this uncertainty, one needs to investigate and identify effective upper and lower boundaries of the level of Fuzziness. For this purpose, Fuzzy c-means (FCM) clustering methodology is investigated to determine the effective upper and lower boundaries of the level of Fuzziness in order to capture the uncertainty generated by this parameter. In this regard, we propose to expand the membership function around important information points of FCM. These important information points are, cluster centers and the mass center. At these points, it is known that, the level of Fuzziness has no effect on the membership values. In this way, we identify the counter-intuitive behavior of membership function near these particular information points. It will be shown that the upper and lower values of the level of Fuzziness can be identified. Hence the uncertainty generated by this parameter can be encapsulated.
Ravit Helled - One of the best experts on this subject based on the ideXlab platform.
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the Fuzziness of giant planets cores
The Astrophysical Journal, 2017Co-Authors: Ravit Helled, David J StevensonAbstract:Giant planets are thought to have cores in their deep interiors, and the division into a heavy-element core and hydrogen–helium envelope is applied in both formation and structure models. We show that the primordial internal structure depends on the planetary growth rate, in particular, the ratio of heavy elements accretion to gas accretion. For a wide range of likely conditions, this ratio is in one-to-one correspondence with the resulting post-accretion profile of heavy elements within the planet. This flux ratio depends sensitively on the assumed solid-surface density in the surrounding nebula. We suggest that giant planets' cores might not be distinct from the envelope and includes some hydrogen and helium, and the deep interior can have a gradual heavy-element structure. Accordingly, Jupiter's core may not be well defined. Accurate measurements of Jupiter's gravitational field by Juno could put constraints on Jupiter's core mass. However, as we suggest here, the definition of Jupiter's core is complex, and the core's physical properties (mass, density) depend on the actual definition of the core and on the planet's growth history.
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the Fuzziness of giant planets cores
arXiv: Earth and Planetary Astrophysics, 2017Co-Authors: Ravit Helled, David J StevensonAbstract:Giant planets are thought to have cores in their deep interiors, and the division into a heavy-element core and hydrogen-helium envelope is applied in both formation and structure models. We show that the primordial internal structure depends on the planetary growth rate, in particular, the ratio of heavy elements accretion to gas accretion. For a wide range of likely conditions, this ratio is in one-to-one correspondence with the resulting post-accretion profile of heavy elements within the planet. This flux ratio depends sensitively on the assumed solid surface density in the surrounding nebula. We suggest that giant planets' cores might not be distinct from the envelope and includes some hydrogen and helium, and the deep interior can have a gradual heavy-element structure. Accordingly, Jupiter's core may not be well-defined. Accurate measurements of Jupiter's gravitational field by Juno could put constraints on Jupiter's core mass. However, as we suggest here, the definition of Jupiter's core is complex, and the core's physical properties (mass, density) depend on the actual definition of the core and on its growth history.
Burhan I Turksen - One of the best experts on this subject based on the ideXlab platform.
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minimax e stable cluster validity index for type 2 Fuzziness
Information Sciences, 2012Co-Authors: Ibrahim Ozkan, Burhan I TurksenAbstract:In this paper, we concentrate on the usage of uncertainty associated with the level of Fuzziness in determination of the number of clusters in FCM for any data set. We propose a MiniMax @e-stable cluster validity index based on the uncertainty associated with the level of Fuzziness within the framework of interval valued Type 2 Fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of Fuzziness. Upper and lower values of the level of Fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as m=2.6 and 1.4, respectively, in our previous studies. Our investigation shows that the stability of cluster centers with respect to the level of Fuzziness is sufficient for the determination of the number of clusters.
-
minimax e stable cluster validity index for type 2 Fuzziness
North American Fuzzy Information Processing Society, 2010Co-Authors: Ibrahim Ozkan, Burhan I TurksenAbstract:Uncertainty is a central part of many data analysis methodologies. Although quantifying the uncertainty has long been discussed, the research on it is still in progress. The level of Fuzziness in fuzzy system modeling is a source of uncertainty which can be classified as a parameter uncertainty. Upper and lower values of the level of Fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as 2.6 and 1.4 respectively in our previous studies. In this paper, we concentrate on the usage of uncertainty associated with the level of Fuzziness in determination of the number of clusters in FCM in any data. We propose MiniMax e-stable cluster validity index based on the uncertainty associated with the level of Fuzziness within the framework of Interval Valued Type 2 Fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of Fuzziness. Our investigation shows that the half range of upper and lower levels of Fuzziness would be enough to determine the optimum number of clusters.
David J Stevenson - One of the best experts on this subject based on the ideXlab platform.
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the Fuzziness of giant planets cores
The Astrophysical Journal, 2017Co-Authors: Ravit Helled, David J StevensonAbstract:Giant planets are thought to have cores in their deep interiors, and the division into a heavy-element core and hydrogen–helium envelope is applied in both formation and structure models. We show that the primordial internal structure depends on the planetary growth rate, in particular, the ratio of heavy elements accretion to gas accretion. For a wide range of likely conditions, this ratio is in one-to-one correspondence with the resulting post-accretion profile of heavy elements within the planet. This flux ratio depends sensitively on the assumed solid-surface density in the surrounding nebula. We suggest that giant planets' cores might not be distinct from the envelope and includes some hydrogen and helium, and the deep interior can have a gradual heavy-element structure. Accordingly, Jupiter's core may not be well defined. Accurate measurements of Jupiter's gravitational field by Juno could put constraints on Jupiter's core mass. However, as we suggest here, the definition of Jupiter's core is complex, and the core's physical properties (mass, density) depend on the actual definition of the core and on the planet's growth history.
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the Fuzziness of giant planets cores
arXiv: Earth and Planetary Astrophysics, 2017Co-Authors: Ravit Helled, David J StevensonAbstract:Giant planets are thought to have cores in their deep interiors, and the division into a heavy-element core and hydrogen-helium envelope is applied in both formation and structure models. We show that the primordial internal structure depends on the planetary growth rate, in particular, the ratio of heavy elements accretion to gas accretion. For a wide range of likely conditions, this ratio is in one-to-one correspondence with the resulting post-accretion profile of heavy elements within the planet. This flux ratio depends sensitively on the assumed solid surface density in the surrounding nebula. We suggest that giant planets' cores might not be distinct from the envelope and includes some hydrogen and helium, and the deep interior can have a gradual heavy-element structure. Accordingly, Jupiter's core may not be well-defined. Accurate measurements of Jupiter's gravitational field by Juno could put constraints on Jupiter's core mass. However, as we suggest here, the definition of Jupiter's core is complex, and the core's physical properties (mass, density) depend on the actual definition of the core and on its growth history.
J. Łęski - One of the best experts on this subject based on the ideXlab platform.
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Application of entropy and energy measures of Fuzziness to processing of ECG signal
Fuzzy Sets and Systems, 1998Co-Authors: E. Czogala, J. ŁęskiAbstract:The paper deals with the application of entropy and energy measure of Fuzziness to processing of ECG signal. After formulation of the problem the notion of entropy measure of Fuzziness is recalled. Next the idea of a fuzzy signal created from the original signal is proposed and the entropy measure of Fuzziness determined for each sample is calculated. The application of this measure to building a detection function of ECG signal is presented. The theoretical considerations have been illustrated with digital processing of the real ECG signal.
