Modified Newton integration neural algorithm for solving the multi-linear M-tensor equation

This paper attends to solve the multi-linear equations with special structure, e.g., the multi-linear M-tensor equation, which frequently appears in engineering applications such as deep learning and hypergraph. For its critical and promising role, there are numbers of resolving schemes devoting to...

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Vydáno v:	Applied soft computing Ročník 96; s. 106674
Hlavní autoři:	Huang, Haoen, Fu, Dongyang, Zhang, Jiazheng, Xiao, Xiuchun, Wang, Guancheng, Liao, Shan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.11.2020
Témata:	Modified Newton integration (MNI) neural algorithm Multi-linear [formula omitted]-tensor equation Noise-suppression ability Multi-linear M-tensor equation Noise-suppression ability Modified Newton integration (MNI) neural algorithm
ISSN:	1568-4946, 1872-9681
On-line přístup:	Získat plný text
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Popis
Shrnutí:	This paper attends to solve the multi-linear equations with special structure, e.g., the multi-linear M-tensor equation, which frequently appears in engineering applications such as deep learning and hypergraph. For its critical and promising role, there are numbers of resolving schemes devoting to obtain a high-performing solution of the multi-linear M-tensor equation. However, few investigations are discovered with noise-suppression ability till now. To be proper with digital devices and further improve the solving effectiveness, it is desirable to design a discrete-time computational algorithm with noise-suppression ability and high-performing property. Inspired by the aforementioned requirements, this paper proposes a modified Newton integration (MNI) neural algorithm for solving the multi-linear M-tensor equation with noise-suppression ability. Additionally, the corresponding robustness analyses on the proposed MNI neural algorithm are provided. Simultaneously, computer simulative experiments are generated to explain the capabilities and availabilities of the MNI neural algorithm in noise suppression. As a result, in terms of noise suppression, the proposed MNI neural algorithm is superior to other related algorithms, such as Newton–Raphson iterative (NRI) algorithm (Ding and Wei, 2016), discrete time neural network (DTNN) algorithm (Wang et al., 2019), and sufficient descent nonlinear conjugate gradient (SDNCG) algorithm (Liu et al., 2020). •An MNI neural algorithm is proposed for multi-linear M-tensor equation with noise-suppression ability.•The MNI neural algorithm is a major breakthrough in Newton-type methods.•Robustness analyses of the MNI neural algorithm are completely provided.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2020.106674