Polygonal approximation using an annealed chaotic Hopfield network

In the paper, the polygonal approximation is regarded as finding the minimum value of restricted function which is defined by the arc-to-chord deviation between the polygon and the curve. We construct a 2D annealed chaotic Hopfield network, ACHN, array with the rows representing the curve points and the columns representing the breakpoints of the approximation polygon. The proposed ACHN overcomes the disadvantage of converging toward local minimum of traditional neural network due to its chaotic, so we can find the approximated polygon more similar to the curve.