scispace - formally typeset
H

Hao Shen

Researcher at Anhui University of Technology

Publications -  296
Citations -  12245

Hao Shen is an academic researcher from Anhui University of Technology. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 54, co-authored 225 publications receiving 8681 citations. Previous affiliations of Hao Shen include Nanjing University of Science and Technology & Yeungnam University.

Papers
More filters
Journal ArticleDOI

Fuzzy-Model-Based Nonfragile Control for Nonlinear Singularly Perturbed Systems With Semi-Markov Jump Parameters

TL;DR: The designed procedures which could well deal with the fragility problem in the implementation of the proposed fuzzy-model-based controller are presented and a technique is developed to estimate the permissible maximum value of singularly perturbed parameter for discrete-time nonlinear semi-Markov jump singular- perturbed systems.
Journal ArticleDOI

Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays

TL;DR: The problem of Hopf bifurcation in the newly-proposed complex-valued neural network model is investigated under the assumption that the activation function can be separated into its real and imaginary parts.
Journal ArticleDOI

Extended Dissipative Control for Singularly Perturbed PDT Switched Systems and its Application

TL;DR: Using the slow-state feedback control method, sufficient conditions to ensure the global uniform exponential stability of the closed-loop PDT SPSSs are derived and a preferable decoupling method deriving the mode-dependent controller gains are given for the first time.
Journal ArticleDOI

Non-Fragile $H_{∞ }$ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation

TL;DR: In this paper, a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used for singularly perturbed coupled neural networks (SPCNNs) affected by nonlinear constraints and gain uncertainties.