A new discrete-time denoising complex neurodynamics applied to dynamic complex generalized inverse matrices

This study introduces a novel discrete-time denoising complex neurodynamics model (referred to as the DTDCN model), which focuses on analyzing and discussing the computation of online dynamic complex generalized inverse matrices under various noisy environments. The proposed DTDCN method has inheren...

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Bibliographic Details
Published in:The Journal of supercomputing 2025, Vol.81 (1), Article 159
Main Authors: Xiang, Qiuhong, Gong, Hongfang, Hua, Cheng
Format: Article
Language:English
Subjects:
Compilers
Computation
Computer Science
Generalized inverse
Interpreters
Inverse matrices
Noise reduction
Processor Architectures
Programming Languages
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Summary:This study introduces a novel discrete-time denoising complex neurodynamics model (referred to as the DTDCN model), which focuses on analyzing and discussing the computation of online dynamic complex generalized inverse matrices under various noisy environments. The proposed DTDCN method has inherent denoising capabilities and high accuracy in online computation. Theoretical analysis has established that the DTDCN model possesses the characteristics of 0-stability, consistency, and convergence. Additionally, experimental results have further reinforced the DTDCN model’s efficacy and superiority in online calculations of dynamic complex generalized inverse matrices under various noisy conditions are further confirmed.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06601-z