Zhang Neural Network Versus Gradient Neural Network for Solving Time-Varying Linear Inequalities

Authors:
Lin Xiao;Yunong Zhang
Affiliations:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China
Venue:
IEEE Transactions on Neural Networks
Year:
2011

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Multi-dimensional Capon spectral estimation using discrete Zhang neural networks

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Different Zhang functions resulting in different ZNN models demonstrated via time-varying linear matrix-vector inequalities solving

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Abstract

By following Zhang design method, a new type of recurrent neural network [i.e., Zhang neural network (ZNN)] is presented, investigated, and analyzed for online solution of time-varying linear inequalities. Theoretical analysis is given on convergence properties of the proposed ZNN model. For comparative purposes, the conventional gradient neural network is developed and exploited for solving online time-varying linear inequalities as well. Computer simulation results further verify and demonstrate the efficacy, novelty, and superiority of such a ZNN model and its method for solving time-varying linear inequalities.