Abstract
A dynamic complex-valued matrix pseudoinversion (DCVMP) is encountered in some special environments, where the system parameters contain the dynamic, magnitude, and phase information. Currently, most of the existing models are employed to the DCVMP under a noise-free workspace. However, the noise perturbation is unavoidable in the practical application scenarios. Therefore, the motivation of this article is to design a computational model for the DCVMP with strong robustness and high-precision computing solutions. To this end, a modified Newton integration (MNI) neural algorithm is proposed for the DCVMP with noise-suppressing ability in this article. Besides, the corresponding convergence proofs on the MNI neural algorithm are provided. Furthermore, the numerical simulations and an application to the estimation of mobile object localization, are demonstrated to illustrate the superiority of the MNI neural algorithm.
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