Analog VLSI and neural systems
Analog VLSI and neural systems
Convex Optimization
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
A recurrent neural network for solving Sylvester equation with time-varying coefficients
IEEE Transactions on Neural Networks
A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation
IEEE Transactions on Neural Networks
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
IEEE Transactions on Neural Networks
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In this paper, a recurrent neural network termed Zhang neural network (ZNN) with a time-varying design parameter γ(t) is developed and presented to solve time-varying quadratic programs subject to time-varying linear equalities. The updated design formula for the ZNN model possesses more generality because the design parameter considered is actually (e.g., in hardware implementation) time-varying, i.e., γ(t). The state vector of such a ZNN model with time-varying design parameter γ(t), can also globally exponentially converge to the theoretical optimal solution pair of the time-varying linear-equality-constrained quadratic program. To achieve superior convergence of the ZNN model, nonlinear activation functions are adopted as well, as compared with the linear-activation-function case. Simulation results substantiate the efficiency of such a ZNN model with a time-varying design parameter γ(t) aforementioned.