Inverse matrix calculation can be considered as an optimization. We have demonstrated that
this problem can be rapidly solved by highly interconnected simple neuron-like analog …

A neural network for matrix inversion is proposed. We show both analytically and by
simulations that this network is guaranteed to be stable and to provide results arbitrarily …

A recurrent neural network for computing inverse matrices in real-time is proposed. The
proposed recurrent neural network consists of n independent subnetworks where n is the …

Two methods of matrix inversion are compared for use in an image reconstruction algorithm.
The first is based on energy minimization using a Hopfield neural network. This is compared …

A complex recurrent neural network (CRNN) is formulated and applied to compute the
complex matrix inverse in real time. Both full rank and rand deficient matrices are …

Three recurrent neural networks are presented for computing the pseudoinverses of rank-
deficient matrices. The first recurrent neural network has the dynamical equation similar to …

We study the use of massively parallel architectures for computing a matrix inverse. Two
different algorithms are reviewed, the traditional approach based on Gaussian elimination …

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 …

Matrix inversion is a general problem in a wide variety of applications. The problem
becomes even more challenging when an efficient hardware implementation is required. In …

Fixed-point simulation results are used for the performance measure of inverting matrices
using the Cholesky decomposition. The working space is reduced significantly by grouping …