Abstract
In this article, considering the effectiveness and efficiency in solving time-varying problems, a new zeroing neural network (ZNN) is proposed to solve time-varying linear equations with column full rank coefficient matrix. In addition, two novel nonlinear activation functions are developed to enhance the comprehensive performance of the ZNN model. It is demonstrated through theoretical analysis and numerical experiments that the nonlinear activated ZNN model has better noise immunity, and faster prescribed-time convergence speed. Finally, the ZNN method is successfully applied to 2-D and 3-D dynamic positioning, with lower positioning error than the traditional pseudoinverse method.
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