An optimization neural network model with lossy dynamics and time-varying activation functions

The paper is concerned with continuously operating optimization neural networks with lossy dynamics. As the main feature of the neural model time-varying nature of neuron activation functions is introduced. The model presented is general in the sense that it covers the cases of neural networks for combinatorial optimization (Hopfield-like networks) and neural models for optimization problems with continuous decision variables (i.e., Kennedy and Chua´s neural network). Besides the rigorous stability analysis of the proposed neural network it is also highlighted the importance of the lossy dynamics, it is shown how to derive lossy versions of improved Hopfield neural models from it and explored the relations to other optimization neural systems.