ProfessorsProfessors of Universidad Politécnica de MadridDiego Andina

Diego Andina

Universidad Politécnica de Madrid

H-index: 26

Europe-Spain

About Diego Andina

Diego Andina, With an exceptional h-index of 26 and a recent h-index of 15 (since 2020), a distinguished researcher at Universidad Politécnica de Madrid, specializes in the field of Bioinspired Algorithms, System of Systems Engineering, Data Driven Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Competitive Perceptrons: The Relevance of Modeling New Bioinspired Properties Such as Intrinsic Plasticity, Metaplasticity, and Lateral Inhibition of Rate-Coding Artificial Neurons

The Art of Editing a High Impact Scientific Journal—The Case of IJNS

AMSOM: Artificial metaplasticity in SOM neural networks—Application to MIT-BIH arrhythmias database

Tackling business intelligence with bioinspired deep learning

Diego Andina Information

University	Universidad Politécnica de Madrid
Position	(UPM)
Citations(all)	2661
Citations(since 2020)	954
Cited By	2121
hIndex(all)	26
hIndex(since 2020)	15
i10Index(all)	59
i10Index(since 2020)	20
Email	Access Email
University Profile Page	Universidad Politécnica de Madrid
Google Scholar	View Google Scholar Profile

Diego Andina Skills & Research Interests

Bioinspired Algorithms

System of Systems Engineering

Data Driven Learning

Top articles of Diego Andina

Title	Journal	Author(s)	Publication Date
Competitive Perceptrons: The Relevance of Modeling New Bioinspired Properties Such as Intrinsic Plasticity, Metaplasticity, and Lateral Inhibition of Rate-Coding Artificial Neurons	Biomimetics	Diego Andina	2023/11/23
The Art of Editing a High Impact Scientific Journal—The Case of IJNS		Diego Andina	2021/12/13
AMSOM: Artificial metaplasticity in SOM neural networks—Application to MIT-BIH arrhythmias database	Neural Computing and Applications	Santiago Torres-Alegre Juan Fombellida Juan Antonio Piñuela-Izquierdo Diego Andina	2020/9
Tackling business intelligence with bioinspired deep learning	Neural Computing and Applications	Juan Fombellida Irene Martín-Rubio Santiago Torres-Alegre Diego Andina	2020/9