CNN with Multi-Level Hysteresis Quantization Output

This paper presents a novel class of cellular neural networks, where the output is given by the multilevel hysteresis quantization function. Since each cell of elementary CNN has bi-stable piecewise linear function, the image processing is restricted to the black-and-white case. Hence, the architecture provided in this paper would extend availability of CNN. Especially, it is extremely useful for image intensity conversion. In this paper, the Lyapunov stability of CNN with multilevel hysteresis quantization output is proven and the computer simulation shows good convergence property of the CNN.