Following the idea of using first-order time derivatives, this paper presents a general
recurrent neural network (RNN) model for online inversion of time-varying matrices. Different …

In the previous work, Zhang et al. developed a special type of recurrent neural networks
called Zhang neural network (ZNN) with continuous-time and discrete-time forms for 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 …

In this paper, we present, develop and investigate a special kind of recurrent neural network
termed Zhang neural network (ZNN) for time-varying matrix inversion. Comparing with the …

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An improved activation function, termed extended sign-bi-power (Esbp), is proposed. An
extension of the Li zeroing neural network (ELi-ZNN) based on the Esbp activation is …

The problem of solving algebraic Riccati equations (AREs) and certain linear matrix
equations which arise from the ARE frequently occur in applied and pure mathematics …

As inspired by revising (Zhang and Ge, 2003), the traditional gradient-based neural system
(also termed analog computer (Manherz et al., 1968)) for matrix inversion is re-visited by …

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 …

A type of recurrent neural networks called Zhang neural network (ZNN) is presented and
investigated to provide an online solution to the time-varying linear matrix equation, A (t) X (t) …