Outlier-resistant variance-constrained $\mathit{H}_{\infty }$ state estimation for time-varying recurrent neural networks with randomly occurring deception attacks

IF 4.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computing & Applications Pub Date : 2023-03-12 DOI:10.1007/s00521-023-08419-x

Yan Gao, Jun Hu, Huijun Yu, Junhua Du, Chaoqing Jia

引用次数: 0

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随机欺骗攻击时变递归神经网络的抗离群方差约束$\mathit{H}_{\infty }$状态估计

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neural Computing & Applications 工程技术-计算机：人工智能

CiteScore

11.40

自引率

8.30%

发文量

1280

审稿时长

6.9 months

期刊介绍： Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.