This paper presents a novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Under the condition that the Hessian matrix of the ass...
This paper incorporates robustness into neural network modeling and proposes a novel two-phase robustness analysis approach for determining the optimal feedforward neural network (...
Networks estimating probability density are usually based on radial basis function of the same type. Feature Space Mapping constructive network based on separable functions, optimi...
Wlodzislaw Duch, Rafal Adamczak, Geerd H. F. Dierc...
— In this paper, a neural network approach is presented to expand the Pareto-optimal front for multiobjective optimization problems. The network is trained using results obtained...
In recent years, the evolution of artificial neural networks or neuroevolution has brought promising results in solving difficult reinforcement learning problems. But, like standa...