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Analysis of institutional authors

Barrios Rolania, DoloresAuthorManrique, DanielCorresponding Author

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June 9, 2019
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Multilayered neural architectures evolution for computing sequences of orthogonal polynomials

Publicated to: ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE. 84 (3-4): 161-184 - 2018-12-01 84(3-4), DOI: 10.1007/s10472-018-9601-2

Authors:

Rolanía, DB; Martínez, GD; Manrique, D
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Affiliations

Teoría de Aproximación Constructiva y Aplicaciones. Universidad Politécnica de Madrid - Author
Univ Politecn Madrid, Dept Inteligencia Artificial, ETSI Informat, Madrid, Spain - Author
Univ Politecn Madrid, ETS Ingn Civil, Madrid, Spain - Author
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Abstract

This article presents an evolutionary algorithm to autonomously construct full-connected multilayered feedforward neural architectures. This algorithm employs grammar-guided genetic programming with a context-free grammar that has been specifically designed to satisfy three important restrictions. First, the sentences that belong to the language produced by the grammar only encode all valid neural architectures. Second, full-connected feedforward neural architectures of any size can be generated. Third, smaller-sized neural architectures are favored to avoid overfitting. The proposed evolutionary neural architectures construction system is applied to compute the terms of the two sequences that define the three-term recurrence relation associated with a sequence of orthogonal polynomials. This application imposes an important constraint: training datasets are always very small. Therefore, an adequate sized neural architecture has to be evolved to achieve satisfactory results, which are presented in terms of accuracy and size of the evolved neural architectures, and convergence speed of the evolutionary process.
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Keywords

39a0542c0568q3268t0568t20Artificial neural networksEvolutionary computationFeedforward networksGrammar-guided genetic programmingOptimizationOrthogonal polynomials

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, and although the journal is classified in the quartile Q3 (Agencia WoS (JCR)), its regional focus and specialization in Mathematics, Applied, give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • WoS: 3
  • Scopus: 3
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Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/90404/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 61
  • Downloads: 46
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (BARRIOS ROLANIA, MARIA DOLORES) and Last Author (MANRIQUE GAMO, DANIEL).

the author responsible for correspondence tasks has been MANRIQUE GAMO, DANIEL.

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Awards linked to the item

This work was partially supported by research grant MTM2014-54053-P of Ministerio de Economia y Competitividad, Spain.
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