Work

Taylor-Type Direct-Discrete-Time Integral Recurrent Neural Network with Noise Tolerance for Discrete-Time-Varying Linear Matrix Problems with Symmetric Boundary Constraints

Year: 2025

Type: article

Abstract: Various types of noise interference often challenge the solution of discrete time-varying linear matrix problems with boundary constraints in practical engineering applications. To address this issue, this paper proposes a Taylor-type direct-discrete-time integral noise-immune recurrent neural network (TD-IRNN) model. Specifically, the model is constructed by transforming the bounded discrete linear matrix problem into a unified discrete matrix formulation and incorporating an error accumulation term during the design process. The proposed TD-IRNN model not only eliminates the need for continuous-environment conversion but also significantly enhances its noise immunity. Theoretical analysis demonstrates that the model exhibits excellent convergence and stability under different noise conditions. Numerical experiments and a robotic manipulator trajectory tracking experiment further validate the effectiveness and practical applicability of the TD-IRNN model in complex environments.

Source: Symmetry

DOI: 10.3390/sym17111975

Authors: Yuhuan Chen, Xuan Li, Jie Chen, Chenfu Yi, Yang Li

Institutions: Guangzhou Maritime College, Guangdong Polytechnic Normal University, Jiangxi University of Technology, Jiangxi University of Science and Technology

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province