Abstract
The paper is concerned with continuously operating optimization neural networks with lossy dynamics. As the main feature of the neural model, the 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., the Kennedy and Chua neural network). Besides the rigorous stability analysis of the proposed neural network, we also show how to derive lossy versions of improved Hopfield neural models from it and explore the relations to other optimization neural systems.
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