Stability problems of continuous-time recurrent neural networks have been extensively
studied, and many papers have been published in the literature. The purpose of this paper is …

Artificial neural network (ANN) is the backbone of machine learning, specifically deep
learning. The interpolating and learning ability of an ANN makes it an ideal tool for …

Software systems evolve over time due to changes in requirements, optimization of code,
fixes for security and reliability bugs etc. Code churn, which measures the changes made to …

Since the last decade, several complex-valued neural networks have been developed and
applied in various research areas. As an extension of real-valued recurrent neural networks …

Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an
experimentally proven new computing paradigm. Analogic cellular computers based on …

The paper introduces a general class of neural networks where the neuron activations are
modeled by discontinuous functions. The neural networks have an additive interconnecting …

In this paper, we investigate the global stability of quaternion-valued neural networks
(QVNNs) with time-varying delays. On one hand, in order to avoid the noncommutativity of …

In this paper, without transforming the second order inertial neural networks into the first
order differential systems by some variable substitutions, asymptotic stability and …

The paper considers a class of additive neural networks where the neuron activations are
modeled by discontinuous functions or by continuous non-Lipschitz functions. Some tools …

In this paper, the global exponential stability and periodicity for a class of reaction–diffusion
delayed recurrent neural networks with Dirichlet boundary conditions are addressed by …