List of Titles

Authors: He, Xing , Li, Chuandong , Huang, Tingwen , Li, Chaojie , Huang, Junjian
Date: 2014
Type: Text , Journal article
Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 25, no. 4 (April 2014 2014), p. 824-830
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Description: In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.

Authors: He, Xing , Li, Chuandong , Huang, Tingwen , Li, Chaojie
Date: 2013
Type: Text , Journal article
Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 24, no. 6 (2013), p. 1001-1007
Full Text: false
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Description: This brief reports a retarded functional differential equation modeling tri-neuron network with time delay. The Bogdanov-Takens (B-T) bifurcation is investigated by using the center manifold reduction and the normal form method. We get the versal unfolding of the norm forms at the B-T singularity and show that the model can exhibit pitchfork, Hopf, homoclinic, and double-limit cycles bifurcations. Some numerical simulations are given to support the analytic results and explore chaotic dynamics. Finally, an algorithm is given to show that chaotic tri-neuron networks can be used for encrypting a color image. © 2012 IEEE.
Description: 2003011033

Authors: He, Xing , Li, Chuandong , Huang, Tingwen , Li, Chaojie
Date: 2012
Type: Text , Journal article
Relation: Nonlinear Analysis : Real World Applications Vol. 14, no. 2 (2012), p. 1191-1202
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Description: In this paper, a delayed neural network model with unidirectional coupling is considered. Zero-Hopf bifurcation is studied by using the center manifold reduction and the normal form method for retarded functional differential equation. We get the versal unfolding of the norm form at the zero-Hopf singularity and show that the model can exhibit pitchfork, Hopf bifurcation, and double Hopf bifurcation is also found to occur in this model. Some numerical simulations are given to support the analytic results. © 2012 Elsevier Ltd. All rights reserved.

Authors: He, Xing , Li, Chuandong , Huang, Tingwen , Li, Chaojie
Date: 2014
Type: Text , Journal article
Relation: Neural Networks Vol. 51, no. May (2014), p. 17-25
Full Text: false
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Description: In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. © 2013 Elsevier Ltd.

Authors: He, Xing , Huang, Junjian , Li, Chaojie
Date: 2018
Type: Text , Conference proceedings , Conference paper
Relation: 2018 15th International Conference on Control, Automation, Robotics and Vision; Singapore, Singapore; 18th-21st November 2018 p. 135-139
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Description: In this brief, considering the inertial term into first order neural networks(NNs), an inertial NN(INN) modeled by means of a differential inclusion is proposed for solving linear complementarity problem with P-0 matrix. Compared with existing NNs, the presence of the inertial term allows us to overcome some drawbacks of many NNs, which are constructed based on the steepest descent method, and this model is more convenient for exploring different optimal solution. It is proved that the proposed NN is stable in the sense of Lyapunov and any equilibrium of our NN is the optimal solution of LCP with P-0 matrix. Simulation results on two numerical examples show the effectiveness and performance of the proposed neural network.

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