Zeroing neural network approaches for computing time-varying minimal rank outer inverse
APPLIED MATHEMATICS AND COMPUTATION(2024)
Abstract
Generalized inverses are extremely effective in many areas of mathematics and engineering. The zeroing neural network (ZNN) technique, which is currently recognized as the state-of-the-art approach for calculating the time-varying Moore-Penrose matrix inverse, is investigated in this study as a solution to the problem of calculating the time-varying minimum rank outer inverse (TV-MROI) with prescribed range and/or TV-MROI with prescribed kernel. As a result, four novel ZNN models are introduced for computing the TV-MROI, and their efficiency is examined. Numerical tests examine and validate the effectiveness of the introduced ZNN models for calculating TV-MROI with prescribed range and/or prescribed kernel.
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Key words
Matrix equation,Zeroing neural network,Generalized inverse,Dynamic system,Minimal rank outer inverse
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