Meta-Heuristic Optimization Methods for Quaternion-Valued Neural Networks

نویسندگان

چکیده

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven surge applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues high-dimensionality, they all induce cost in terms network size or computational runtime. work examines the use quaternions, form hypercomplex numbers, networks. The constructed demonstrate ability quaternions encode data an efficient structure, showing that reduce number total trainable parameters compared their equivalents. Finally, this introduces novel training algorithm using meta-heuristic approach bypasses need for analytic quaternion loss activation functions. allows broader functions over current presents proof-of-concept future work.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2021

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math9090938