Topics in matrix analysis
Analog VLSI and neural systems
Analog VLSI and neural systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
An Efficient Parallel Algorithm for the Solution of Large Sparse Linear Matrix Equations
IEEE Transactions on Computers
Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks
Expert Systems with Applications: An International Journal
Mutual complement between statistical and neural network approaches for rock magnetism data analysis
Expert Systems with Applications: An International Journal
Predicting effect of physical factors on tibial motion using artificial neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hardware implementation of CMAC neural network with reduced storage requirement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A digital hardware pulse-mode neuron with piecewise linear activation function
IEEE Transactions on Neural Networks
Deterministic convergence of an online gradient method for BP neural networks
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
IEEE Transactions on Neural Networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a class of Zhang neural networks (ZNNs) are developed and analyzed on convergence properties. Different from conventional gradient-based neural networks (GNNs), such ZNN is designed based on the idea of measuring the time-derivation information of time-varying coefficients. The general framework of such a ZNN, together with its variant forms, is presented and investigated. The resultant ZNN model activated by linear functions possesses global exponential convergence to the time-varying equilibrium point. By employing proposed new smooth nonlinear odd-monotonically increasing activation functions, superior convergence could be achieved. Computer-simulation examples substantiate the efficacy of such a ZNN model in the context of solution of time-varying generalized linear matrix equations.