| Grant number: | 22/01831-2 |
| Support Opportunities: | Regular Research Grants |
| Start date: | July 01, 2022 |
| End date: | June 30, 2024 |
| Field of knowledge: | Physical Sciences and Mathematics - Mathematics - Applied Mathematics |
| Principal Investigator: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Grantee: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Host Institution: | Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
Abstract
Artificial neural networks (ANNs), including convolutional neural networks, have demonstrated outstanding results in several application areas, including image detection and classification. Concomitantly, hypercomplex-valued neural networks showed competitive or superior performance but with fewer parameters than their equivalent neural networks defined on the real numbers. Among the advantages of hypercomplex-valued ANNs, reducing the number of parameters and treating multiple values as a single entity stand out. Despite significant advances in hypercomplex-valued ANNs, most research in the area focuses on networks based on complex numbers and quaternions. However, alternative hypercomplex number algebras such as hyperbolic numbers, tessarines, and coquaternions can result in efficient hypercomplex-valued ANNs. In this context, this research project aims to investigate ANNs defined in alternative hypercomplex number algebras, considering both theoretical and practical aspects of these models. (AU)
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