Shihua Fu

dblp:216/4730 · DBLP profile ↗
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11ranked-venue papers
2as first author
11since 2021 · last 2026
0000-0003-0569-3283ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 1 first-author · 5 since 2021Databases, data management, data science and information retrieval · 3 · 3 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Equilibrium existence and convergence of congestion games with stochastic disturbances
Shihua Fu, Jianjun Wang 0004, Zhiru Wang, Carmen Del Vecchio, Jianli Zhao 0001
Sci. China Inf. Sci.1
2026 Stability of large-scale probabilistic Boolean networks via network aggregation
Shihua Fu, Jianjun Wang 0004, Renato De Leone, Jianwei Xia
Neural Networks2
2026 Rapid recognition of partially occluded faces via semi-tensor product compressed sensing
Jun-e Feng, Shihua Fu
Pattern Recognit.3
2026 Asynchronous Controllability of Non-Homogeneous Markov Switch Generalized Asynchronous Boolean Control Networks With Deterministic Dwell Time
abstract
This study investigates the asynchronous controllability of non-homogeneous Markov switch generalized asynchronous Boolean control networks (NMHGABCNs) and random switching signals in these networks, aiming to follow a non-homogeneous Markov process. The controllability of the proposed networks is achieved using the discrepancy between the Markov chain mode and the control mode. Assisted by the semi-tensor product (STP), the algebraic forms of the NMHGABCNs are obtained, and the sufficient and necessary criteria for their asynchronous controllability are derived. The effectiveness of controllability is demonstrated through two examples, which validate the theoretical results.
Hao Zhang 0061, Xianghui Su, Shihua Fu, Jie Zhong 0005
IEEE Trans. Comput. Biol. Bioinform.3
2025 Dimensionality Reduction Method for the Output Regulation of Boolean Control Networks
abstract
This article proposes a dimensionality reduction approach to study the output regulation problem (ORP) of Boolean control networks (BCNs), which has much lower computational complexity than previous results. First, an auxiliary system which is much smaller in scale than the augmented system in previous approach is constructed. By analyzing the set stabilization of the auxiliary system as well as the original BCN, a necessary and sufficient condition to detect the solvability of the ORP is presented. Second, a method to design the state feedback controls for the ORP is proposed. Finally, two biological examples are given to demonstrate the effectiveness and advantage of the obtained new results.
Shihua Fu, Jun-e Feng, Jianjun Wang 0004, Jinfeng Pan
IEEE Trans. Neural Networks Learn. Syst.1
2025 Iterative Algorithms for Set Stabilization of Probabilistic Boolean Control Networks and Applications in State-Based Games
Jun-e Feng, Renato De Leone, Shihua Fu, Jianwei Xia
IEEE Trans. Syst. Man Cybern. Syst.4
2024 Minimum observability of probabilistic Boolean networks
Shihua Fu, Liyuan Xia, Jianjun Wang 0004
Inf. Sci.2
2024 Robust stability of Boolean networks with data loss and disturbance inputs
Jianwei Xia, Jun-e Feng, Shihua Fu
Neural Networks4
2023 Leader-follower output consensus of multiagent systems over finite fields
Miao Yu 0025, Jianwei Xia, Jun-e Feng, Shihua Fu, Hao Shen 0001
Neurocomputing4
2023 Sampled-data state feedback control design for evolutionary threshold public goods games on coupled networks
Shihua Fu, Jianjun Wang 0004, Jun-e Feng, Jianli Zhao 0001
Inf. Sci.2
2023 Optimal output tracking of Boolean control networks
Yanan Pan, Shihua Fu, Jianjun Wang 0004, Weihai Zhang
Inf. Sci.2