Zeroing neural network approaches for computing time-varying minimal rank outer inverse

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. •The TV-MROI with PR and/or with PK problems are addressed for the first time through the ZNN approach.•With the purpose of computing the TV-MROI with PR and/or PK, four novel ZNN models are introduced.•A theoretical investigation is performed on the models to validate them.