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Xiaofeng Liao
Researcher at Chongqing University
Publications - 496
Citations - 18524
Xiaofeng Liao is an academic researcher from Chongqing University. The author has contributed to research in topics: Exponential stability & Artificial neural network. The author has an hindex of 67, co-authored 457 publications receiving 16381 citations. Previous affiliations of Xiaofeng Liao include Chongqing University of Posts and Telecommunications & University of Electronic Science and Technology of China.
Papers
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Stability of bifurcating periodic solutions for a single delayed inertial neuron model under periodic excitation
TL;DR: In this paper, the authors investigated the dynamical characteristics of a single inertial neuron model with time delay under periodic external stimuli and showed that the system will lose its stability when the time delay is increased and will give rise to a quasi-periodic motion and chaos under the interaction of the periodic excitation.
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Robust stability for uncertain genetic regulatory networks with interval time-varying delays
TL;DR: Some new delay-range-dependent and delay-derivative-dependent/independent stability criteria are derived by employing some free-weighting matrices and linear matrix inequalities to address the problem of robust stability of uncertain genetic regulatory networks with interval time-varying delays.
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Chaos quasisynchronization induced by impulses with parameter mismatches.
TL;DR: A new definition for global quasisynchronization is introduced and used to analyze the synchronous behavior of coupled chaotic systems in the presence of parameter mismatch, and a global synchronization error bound together with a sufficient condition is derived.
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Stability and Hopf bifurcation of a complex-valued neural network with two time delays
TL;DR: In this paper, a class of complex-valued neural networks with two time delays is considered and the activation function can be expressed by separating into its real and imaginary part and regarding the sum of time delays as a bifurcating parameter.
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Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction.
TL;DR: In this paper, a patient-specific EEG seizure predictor is proposed by using a spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme.