A Fully Complex‐Valued Gradient Neural Network for Rapidly Computing Complex‐Valued Linear Matrix Equations. Issue 6 (1st November 2017)

• Record Type: Journal Article
• Authors: Xiao, Lin; Lu, Rongbo
• Is Part Of: Chinese journal of electronics. Volume 26:Issue 6(2017)
• Page Start: 1194
• Page End: 1197
• Publication Date: 2017-11-01
• Subjects: Gradient neural network (GNN) -- Complex domain -- Complex‐valued linear matrix equation -- Simulation verification; matrix algebra -- neural nets; fully complex‐valued gradient neural network -- complex‐valued linear matrix equations -- real‐valued neural network -- real‐valued linear matrix equations -- fully complex‐valued GNN -- complexities -- convergence analysis; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.06.007
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : This paper concerns online solution of complex‐valued linear matrix equations in the complex domain. Differing from the real‐valued neural network, which is only designed for solving real‐valued linear matrix equations in the real domain, a fully complex‐valued Gradient neural network (GNN) is developed for computing complex‐valued linear matrix equations. The fully complex‐valued GNN model has the merit of reducing the unnecessary complexities in theoretical analysis and realtime computation, as compared to the real‐valued neural network. Besides, the convergence analysis of the proposed complex‐valued GNN model is presented, and simulation experiments are performed to substantiate the effectiveness and superiority of the proposed complex‐valued GNN model for online computing the complex‐valued linear matrix equations in the complex domain.