International Journal of Research and Scientific Innovation (IJRSI)
N-Power Stable and Robust Operator Models in Computational Spaces
Published December 18, 2025 • Vol. 12, Issue 11, pp. 1422–1425Open Access
DOI: 10.51244/IJRSI.2025.12110126
Abstract
In this article, n-power stable, n-power robust, quasi-stable, and quasi-robust operator models are characterized in computational spaces. These classes of operators, originally studied in mathematical Fock spaces, are extended to applications in Computer Technology. In particular, we establish how such operator conditions contribute to the stability of iterative algorithms, normalization in machine learning, bounded mappings in signal and image processing and operator evolution in quantum computing. The analysis shows that the operator-theoretic framework ensures convergence, robustness and error control in modern computational pipelines.
Keywords: Composition operator, Data transformation, Machine learning stability
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 12, Issue 11 |
| Pages | 1422–1425 |
| Publication date | December 18, 2025 |
| DOI | 10.51244/IJRSI.2025.12110126 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Dr. N. Sivamani, Dr. P. Selvanayaki, & V. Aarthika (2025). N-Power Stable and Robust Operator Models in Computational Spaces. International Journal of Research and Scientific Innovation (IJRSI), 12(11), 1422-1425. https://doi.org/10.51244/IJRSI.2025.12110126
BibTeX
@article{Dr2025,
title = {N-Power Stable and Robust Operator Models in Computational Spaces},
author = {Dr. N. Sivamani and Dr. P. Selvanayaki and V. Aarthika},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {12},
number = {11},
pages = {1422--1425},
year = {2025},
doi = {10.51244/IJRSI.2025.12110126},
publisher = {RSIS International}
}