Wang proposed a gradient-based neural network (GNN) to solve online matrix-inverses.
Global asymptotical convergence was shown for such a neural network when applied to …

Online solution to time-varying matrix inverse is further investigated by proposing a new
design formula, which can accelerate Zhang neural network (ZNN) to finite-time …

Different from gradient-based neural networks, a special kind of recurrent neural network
(RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is …

Different from gradient-based neural networks, a special kind of recurrent neural network
has recently been proposed by Zhang et al for online matrix inversion. Such a neural …

Encouraged by superior convergence performance achieved by a recently-proposed hybrid
of recursive neural dynamics for online matrix inversion, we investigate its robustness …

Differing from gradient-based neural networks (GNN), a special kind of recurrent neural
network has recently been proposed by Zhang et al. for real-time inversion of time-varying …

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient
neural network (GNN) is recognized as an effective method for static matrix inversion with …

Matrix inversion often arises in the fields of science and engineering. Many models for matrix
inversion usually assume that the solving process is free of noises or that the denoising has …

To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural
networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural …

In order to compute time-varying matrix inversion faster, a novel exponential-enhanced-type
varying-parameter recurrent neural network (EVP-RNN) is proposed and investigated in this …