An approach to structured multilinear modeling with relaxed Boolean output functions

被引：1

作者：

Engels, Marah ^{[1
]}

Lichtenberg, Gerwald ^{[2
]}

Knorn, Steffi ^{[3
]}

机构：

[1] HAW Hamburg, Competence Ctr Renewable Energies & Energy Effici, Hamburg, Germany

[2] HAW Hamburg, Fac Life Sci, Hamburg, Germany

[3] TU Berlin, Sch Proc Sci & Engn, Berlin, Germany

来源：

IFAC PAPERSONLINE | 2023年 / 56卷 / 02期

关键词：

Boolean differentials; ternary vector lists; Zhegalkin polynomials; model structure; reduction; multilinear models; tensor decomposition;

D O I：

10.1016/j.ifacol.2023.10.314

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new structured network modeling approach based on binary node indexing and Boolean differentials is introduced. Orthogonal ternary vector lists serve as a structured representation of the model's state dynamics. Tensor decomposition methods are enabled by relaxation to continuous Zhegalkin polynomials due to their inherently multilinear nature. A periodic example is used to demonstrate how a low-dimensional state space can provide a large number of linearly independent outputs. Copyright (c) 2023 The Authors.

引用

页码：7920 / 7925

页数：6

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