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Modified Newton integration neural algorithm for solving the multi-linear M-tensor equation
- Source :
- Applied Soft Computing. 96:106674
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- 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).
- Subjects :
- 0209 industrial biotechnology
Hypergraph
Artificial neural network
business.industry
Computer science
Deep learning
02 engineering and technology
Nonlinear conjugate gradient method
020901 industrial engineering & automation
Discrete time and continuous time
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Tensor
business
Algorithm
Software
Subjects
Details
- ISSN :
- 15684946
- Volume :
- 96
- Database :
- OpenAIRE
- Journal :
- Applied Soft Computing
- Accession number :
- edsair.doi...........0ba86220ba42913f4c0aa467e2661f0f