基于乐观值的不确定非因果系统零和博弈的鞍点解

IF 1.2 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY Journal of Industrial and Management Optimization Pub Date : 2023-01-01 DOI:10.3934/jimo.2023135
Xin Chen, Yan Wang, Fuzhen Li, Yu Shao
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引用次数: 0

摘要

不确定非因果系统(uncs)是不确定的奇异系统,它被认为是规则的。本研究运用乐观价值准则,对cscs的二人零和博弈进行了研究。首先介绍了一种将考虑线性控制项的线性不确定非因果系统(UNCS)转化为包含两类不确定差分方程的子系统的方法。推导了求解受线性uncs约束的TPZSG的递推方程。利用递归方程,给出了求解线性uncs约束下的TPZSG问题的算法,并通过一个算例说明了如何应用该算法求解这类博弈的鞍点解和平衡值。为了扩展这些结果,我们提供了求解非线性uncs下TPZSGs的相应方程。此外,通过求解方程,给出了考虑二次控制项的非线性UNCS的鞍点解和TPZSG的平衡值。
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Saddle-point solution to zero-sumgame for uncertain noncausal systems based on optimistic value
Uncertain noncausal systems (UNCSs) are uncertain singular systems that are supposed to be regular along.This study investigates two-player zero-sum games (TPZSGs) for UNCSs using the optimistic value criterion. The first step is to introduce a method to convert a linear uncertain noncausal system (UNCS) considering linear control terms into subsystems including two kinds of uncertain difference equations. Recurrence equations are derived for tackling a TPZSG subject to linear UNCSs. Using recurrence equations, we give an algorithm for solving the TPZSG subject to linear UNCSs and show how to apply the algorithm to find the saddle-point solution and the equilibrium value of this kind of game by a numerical example. To expand on these results, we provide the corresponding equations for solving TPZSGs subject to nonlinear UNCSs. Moreover, we give the saddle-point solution and equilibrium value of a TPZSG for a nonlinear UNCS considering quadratic control terms by solving the equations.
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来源期刊
CiteScore
2.50
自引率
15.40%
发文量
207
审稿时长
18 months
期刊介绍: JIMO is an international journal devoted to publishing peer-reviewed, high quality, original papers on the non-trivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques.
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