Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Fast sparse approximation of extreme learning machine
Neurocomputing
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A gradient system with discontinuous righthand side that solves an underdetermined system of linear equations in the L1 norm is presented. An upper bound estimate for finite time convergence to a solution set of the system of linear equations is shown by means of the Persidskii form of the gradient system and the corresponding nonsmooth diagonal type Lyapunov function. This class of systems can be interpreted as a recurrent neural network and an application devoted to solving least squares support vector machines (LS-SVM) is used as an example.