Work

A Principal Component Estimation Algorithm with Adaptive Learning Rate and Its Convergence Analysis

Year: 2025

Type: article

Abstract: The deterministic discrete-time method is a dominant approach for analyzing neural network algorithms. To address the issue where conventional convergence conditions impose stringent restrictions on the range of learning factors, this paper proposes a principal component estimation algorithm with an adaptive learning factor, which guarantees global convergence. The convergence of the algorithm is analyzed using the deterministic discrete-time method, and conditions ensuring convergence are established. Unlike convergence conditions for other algorithms, the proposed algorithm’s convergence conditions eliminate restrictions on the learning factor, thereby extending its feasible range. Simulation results demonstrate that the proposed algorithm effectively resolves ill-conditioned matrix problems. When compared with existing algorithms, the proposed algorithm exhibits significantly faster convergence speed than several current methods.

Source: Applied Sciences

DOI: 10.3390/app152111826

Authors: Yingbin Gao, Haidi Dong, Yuan Zhou, Zhongying Xu, Jing Li, Kai Jin

Institutions: China Electronics Technology Group Corporation, Naval University of Engineering, PLA Rocket Force University of Engineering

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China