Comparison of genetic algorithm systems with neural network and statistical techniques for analysis of cloud structures in midlatitude storm systems

Parikh, Jo Ann; DaPonte, John S.; Vitale, Joseph N.; Tselioudis, George

doi:10.1016/s0167-8655(97)00115-3

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…These sorts of images are notoriously difficult to classify because natural scenes do not have the strong identifying characteristics as do, say, buildings or soccer fields. 2,3 Nevertheless, even for oceanographic scenes heavily obscured by clouds, classification results were highly accurate for this class of images.…”

Section: Introductionmentioning

confidence: 93%

“…This is graphically shown in the top half of Figure 4. The functional mapping, R R2 s R2 , 2 can be constructed easily since predicates perpendicular-to and parallel-to use the function f and the predicate area-greater-than uses the function f . In this 1 2 simple example, the semantic net S SN N s SN closely corresponds to R1 , and 2 2 Figure 4 shows the useful parts of the semantic net for a typical chair using the three predicates.…”

Section: žmentioning

confidence: 99%

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Genetic algorithms for scene interpretation from prototypical semantic description

Prabhu

Buckles

Petry

2000

Int. J. Intell. Syst.

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Section: Introductionmentioning

confidence: 93%

Section: žmentioning

confidence: 99%

Genetic algorithms for scene interpretation from prototypical semantic description

Prabhu

Buckles

Petry

2000

Int. J. Intell. Syst.

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“…A majority of these classification tasks are approached as supervised learning problems, in which previously classified samples from a historical dataset are represented by a characteristic feature vector and serve as a set of training samples. Parikh et al (1997) apply neural networks, genetic algorithms, and statistical methods to the recognition and tracking of midlatitude cloud systems in cloud-top pressure datasets. Baldwin et al (2005) apply a nearest-neighbor algorithm to the classification of rainfall systems in radar data.…”

Section: Related Researchmentioning

confidence: 99%

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Bankert

Mitrescu

Miller

et al. 2009

Journal of Applied Meteorology and Climatology

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Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification. * Deceased.

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“…The Rule of the Nearest Neighbor (RNN) , Support Machine Vector Manuscript received January 5,2009 (SVM), Artificial Neural Networks (ANN), among others, are techniques in the Pattern Recognition (PR) area of the Artificial Intelligence successfully applied in tasks where it is important to determine the category to which an object belongs, or to identify the existing categories in a set of objects given. They have awakened interest on PR investigators, [3][4][5][6][7], because they do not need an apriori model of the topic to try, for its operation.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for fitting PES data distributions of asphaltene interaction

Gasca

Pacheco

Álvarez

2009

2009 International Joint Conference on Neural Networks

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Neural networks methodology is a tool to get potential energy surface (PES) in cases where there is too much dispersion of data; hence, a binding energy fitting can be found with this methodology on asphaltene-asphaltene molecular interaction. A data distribution of intermolecular pair potential (U AA) interaction in a vacuum between two molecular asphaltenes systems using compass classical force field has been previously reported (Energy Fuels 2006, 20, 195). In the latter, all possible interactions between the species were taken into account. Focusing in their data distribution, we have applied neural networks on the following molecule-molecule geometry orientations with the purpose of obtaining energy vs. contact distance, which is the minimum distance where the interacting species is not equal to zero: i) face-to-face distribution of asphaltene-asphaltene interactions; ii) all geometry asphalteneasphaltene discrete distributions, and iii) the random distribution of asphaltene-asphaltene interactions. Neural networks fit provide a potential energy surface through a function approximation, for a data distribution of high dispersion; hence a binding energy is found with this methodology.

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Comparison of genetic algorithm systems with neural network and statistical techniques for analysis of cloud structures in midlatitude storm systems

Cited by 6 publications

References 12 publications

Genetic algorithms for scene interpretation from prototypical semantic description

Genetic algorithms for scene interpretation from prototypical semantic description

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Neural networks for fitting PES data distributions of asphaltene interaction

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