基于方程 AXA = A 的归零神经网络

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS Information and Computation Pub Date : 2024-07-31 DOI:10.1016/j.ic.2024.105207
Marko D. Petković, Predrag S. Stanimirović
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引用次数: 0

摘要

根据现有的科学研究,还没有基于彭罗斯方程研究用于计算矩阵逆和广义逆的归零神经网络(ZNN)模型的信息。我们的目的是提出一种新的 ZNN 设计,它以彭罗斯矩阵方程为定义基础,旨在寻找时变矩阵逆和伪逆。我们提出了基于第一个彭罗斯方程的新型归零函数(ZF)。我们定义并研究了用于计算时变逆和伪逆的初始 ZNN 设计。此外,还提出了所定义模型的显式形式。研究了所提出的显式动力学在时不变和时变两种情况下的收敛特性。为了验证所获得的理论结果,给出了说明性的模拟结果。
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Zeroing neural network based on the equation AXA = A

According to available scientific research, there is no information that Zeroing Neural Network (ZNN) models for calculating the matrix inverse and generalized inverses have been studied on the basis of Penrose equations. Our intention is to present a new ZNN design which defined on the Penrose matrix equations and whose intention is to find the time-variant matrix inverse and pseudoinverse. We propose a novel Zeroing function (ZF) based on the first Penrose equation AXA=A. The initiated ZNN design for computing the time-varying inverse and the pseudoinverse is defined and investigated. An explicit form of the defined model is also proposed. The convergence properties of the proposed explicit dynamics are investigated in both the time-invariant and time-varying case. Illustrative simulation results are given in order to verify the obtained theoretical results.

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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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