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具有数据丢失和干扰输入的布尔网络的鲁棒稳定性。

Robust stability of Boolean networks with data loss and disturbance inputs.

机构信息

Research Center of Semi-tensor Product of Matrices: Theory and Applications, School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, Shandong, PR China.

Research Center of Semi-tensor Product of Matrices: Theory and Applications, School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, Shandong, PR China; School of Mathematics, Shandong University, Jinan 250100, PR China.

出版信息

Neural Netw. 2024 Nov;179:106504. doi: 10.1016/j.neunet.2024.106504. Epub 2024 Jul 2.

Abstract

This study discusses the robust stability problem of Boolean networks (BNs) with data loss and disturbances, where data loss is appropriately described by random Bernoulli distribution sequences. Firstly, a BN with data loss and disturbances is converted into an algebraic form via the semi-tensor product (STP) technique. Accordingly, the original system is constructed as a probabilistic augmented system, based on which the problem of stability with probability one for the original system becomes a set stability with probability one for the augmented system. Subsequently, certain criteria are proposed for the robust stability of the systems. Moreover, an algorithm is developed to verify the robust set stability of the augmented system based on truth matrices. Finally, the validity of the obtained results is demonstrated by an illustrative example.

摘要

本研究讨论了具有数据丢失和干扰的布尔网络(BN)的鲁棒稳定性问题,其中数据丢失通过适当的随机伯努利分布序列进行描述。首先,通过半张量积(STP)技术将具有数据丢失和干扰的 BN 转换为代数形式。因此,原始系统被构建为一个概率增广系统,基于此,原始系统的概率一致稳定性问题成为增广系统的集合稳定性问题。随后,提出了系统鲁棒稳定性的一些准则。此外,还开发了一种基于真矩阵的增广系统鲁棒集合稳定性验证算法。最后,通过一个实例验证了所得到的结果的有效性。

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