A linear optimization neural network for associative memory

The model considered herein is intrinsically a neural network for solving linear optimization problems with hybrid constraints. But its state is allowed to lie in a general closed convex set spanned by the prototype patterns. The performance of the proposed network is demonstrated by means of simulation of one numerical example.