Improved Gradient Neural Networks for Solving Moore–Penrose Inverse of Full-Rank Matrix
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Being with parallel-computation nature and convenience of hardware implementation, linear gradient neural networks (LGNN) are widely used to solve large-scale online matrix-involved problems. In this paper, two improved GNN (IGNN) models, which are activated by nonlinear functions, are first developed and investigated for Moore-Penrose inverse of full-rank matrix. The global convergence performances of such two models and LGNN models are theoretically analyzed. Two illustrative examples are performed to further demonstrate the theoretical results as well as the feasibility and efficacy of the proposed IGNN models for solving full-rank matrix Moore-Penrose inverse in real time. At last, a robot application example is provided to show the practical utility of the proposed IGNN models.
KeywordsGradient neural network Moore–Penrose inverse Global convergence Parallel-computation
- 9.Huang S, Zhao G, Chen M (2018) Tensor extreme learning design via generalized Moore–Penrose inverse and triangular type-2 fuzzy sets. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3385-5
- 18.Paszkiel S (2017) Characteristics of question of blind source separation using Moore–Penrose pseudoinversion for reconstruction of EEG signal. In: ICA 2017. Advances in Intelligent Systems and Computing, vol 550, pp 393–400Google Scholar
- 23.Wang H, Li J, Liu H (2006) Practical limitations of an algorithm for the singular value decomposition as applied to redundant manipulators. Proc IEEE Conf Robot Autom Mechatron 1:1–6Google Scholar