A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

The issue of complex-valued time-dependent pseudoinverse often exists in science and engineering fields. In the existing studies, many models were presented for solving complex-valued time-dependent pseudoinverse in the noiseless environments. However, the appearance of noise is unavoidable in practice. In this paper, a novel noise-acceptable zeroing neural network (NAZNN) model is first proposed for computing complex-valued time-dependent matrix pseudoinverse with different noise situations. For comparison, the traditional zeroing neural network and the gradient neural network are adopted to complete the same task. Theoretical analyses prove that the proposed NAZNN model obtains the global exponential convergence performance. Moreover, the proposed NAZNN is also proven to obtain strong resistance to various sorts of noise. Finally, the results of numerical experiments further substantiate the theoretical analysis and indicate the effectiveness and superiority of the proposed NAZNN model for computing complex-valued time-dependent matrix pseudoinverse in various kinds of noise.