A symbolic regression-based modelling strategy of AC/DC rectifiers for RFID applications

Čeperić, Vladimir; Bako, Niko; Barić, Adrijan

doi:10.1016/j.eswa.2014.06.021

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…The wide applicability of this approach is attracting researchers from different areas (Sarradj and Geyer, 2014;Ceperic, Bako and Baric, 2014;Barmpalexis et al, 2011;Wu, Chou and Su, 2008;Cai et al, 2006;). SR plays an increasingly important role in many engineering applications, such as signal processing (Yao and Lin, 2009), industrial data analysis , and experimental design of manufacturing systems (Can and Heavey, 2011).…”

Section: Symbolic Regressionmentioning

confidence: 99%

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Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

González

Monte

Porras

2016

Eastern European Economics

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Abstract. Tendency surveys are the main source of agents' expectations. The main aim of this study is twofold. First, we propose a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, we combine the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results in the Czech Republic and Hungary. Finally, we assess the impact of the 2008 financial crisis, finding an improvement in the capacity of agents' expectations in most Central and Eastern European economies to anticipate economic growth after the crisis.

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Section: Symbolic Regressionmentioning

confidence: 99%

“…Empirical modelling via SR with GP is increasingly attracting interest from different areas due to its wide applicability (Cai et al, 2006;Yao andLin, 2009, Vladislavleva et al, 2010;Can and Heavey, 2011;Barmpalexis et al, 2011;Ceperic et al, 2014;Sarradj and Geyer, 2014). Nevertheless, the applications in economics are still few.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

González

Monte

Porras

2016

Eastern European Economics

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“…Due to its versatility, SR is being increasingly used in different areas: from industrial data analysis and the experimental design of manufacturing systems (Can and Heavey, 2011), to signal processing (Yao and Lin, 2009) and other various applications (Barmpalexis et al, 2011;Cai et al, 2006;Ceperic et al, 2014;Sarradj and Geyer, 2014;Wu et al, 2008).…”

Section: Evolutionary Computationmentioning

confidence: 99%

A new approach for the quantification of qualitative measures of economic expectations

2016

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In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. We obtain the best results for Belgium, Norway, Austria, Lithuania, Japan and the United Kingdom.

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“…For the last decade, the ANN models have been used for a variety of regression problems in microwave circuit design such as small and large signal modeling of active devices, temperature‐based models of microwave transistors, modeling of microstrip line, modeling of Minkowski RA, and regression of scattering parameters of a microwave transistor with respect to its direct current (DC) bias conditions . Recently, GP or SR is being used for modeling of alternating current (AC)/DC rectifiers for radio frequency identification (RFID) applications, derivation of an accurate analytical formula for characteristic impedance calculation of a microstrip transmission line, and prediction of reflection phase characteristics of a 3D printable nonuniform RA (NURA)…”

Section: Introductionmentioning

confidence: 99%

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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A symbolic regression-based modelling strategy of AC/DC rectifiers for RFID applications

Cited by 21 publications

References 16 publications

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

A new approach for the quantification of qualitative measures of economic expectations

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

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