Surrogate modeling with Genetic Programming applied to satellite communication and ground stations

Rodríguez, Glen; Velasquez, Ivan; Cachi, Dane; Inga, Dante

doi:10.1109/aero.2012.6187326

Cited by 3 publications

(2 citation statements)

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“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become impracticable or computationally inefficient solutions . Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR) …”

Section: Ai‐based Regression Algorithmsmentioning

confidence: 99%

“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become 62,63 Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR). [64][65][66][67][68][69][70] Symbolic regression has been inspired by "Darwinian theory of evolution," where there is no any calculated solution. Instead, there are a series of potential solutions that simply can be named as population.…”

Section: Symbolic Regressionmentioning

confidence: 99%

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Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Mahoutı

2019

Int J Numerical Modelling

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Reflectarray antennas (RAs) have the ability to combine the advantages of both traditional parabolic reflector and phased array antennas without the need for feed network designs. Microstrip reflectarrays (MRAs) have the advantages of being small size, light weighted, easy to prototyped, high gain, low side-lobe level, and a predetermined radiation pattern. These can be achieved by precise calculation of reflection phase at each RA unit independently with a phase compensation proportional to the distance from the feed. The challenging problem is to have a fast and high accurate unit element to be used in multidimension, multiobjective design optimization. Herein, artificial intelligence algorithms (AIAs) have been used for prediction of reflection phase characterization of an X band MRA unit element with respect to the geometrical design parameters. Firstly, a nonuniform unit RA has been designed in 3D electromagnetic (EM) simulation tool for creating the training validation data sets. Then, the data sets are given to the different types of AIA regression models such as multilayer perceptron, symbolic regression, and convolutional neural network. From the results of the validation data set, it can be concluded that the proposed models have sufficient accuracy that can be used in a computationally efficient design optimization process of a large-scale RA design.

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Section: Ai‐based Regression Algorithmsmentioning

confidence: 99%

“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become 62,63 Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR). [64][65][66][67][68][69][70] Symbolic regression has been inspired by "Darwinian theory of evolution," where there is no any calculated solution. Instead, there are a series of potential solutions that simply can be named as population.…”

Section: Symbolic Regressionmentioning

confidence: 99%