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 …

A neural network based on smoothing approximation is presented for a class of nonsmooth,
nonconvex constrained optimization problems, where the objective function is nonsmooth …

In this paper, the performance of a gradient neural network (GNN), which was designed
intrinsically for solving static problems, is investigated, analyzed and simulated in the …

This article introduces a new class of memristor neural networks (NNs) for solving, in real-
time, quadratic programming (QP) and linear programming (LP) problems. The networks …

Recent papers have pointed out the interest to study convergence in the presence of
multiple equilibrium points (EPs)(multistability) for neural networks (NNs) with nonsymmetric …

This paper considers a class of nonsmooth neural networks with discontinuous hard-limiter
(signum) neuron activations for solving time-dependent (TD) systems of algebraic linear …

In this paper, a general class of neural networks with memristive synapses and multiple
delays is introduced and studied. Within mathematical framework of the Filippov solution …

The paper considers nonsmooth neural networks described by a class of differential
inclusions termed differential variational inequalities (DVIs). The DVIs include the relevant …

In this paper, a one-layer recurrent projection neural network is proposed for solving
pseudoconvex optimization problems with general convex constraints. The proposed …

We propose the design of Physically Unclonable Functions (PUFs) exploiting the nonlinear
behavior of Cellular Neural Networks (CNNs). Our work derives from some theoretical …