A new structure of large-neighborhood cellular nonlinear network (LN-CNN)

Abstract

In this paper, a novel Large-Neighborhood Cellular Nonlinear Network (LN-CNN) structure is proposed and analyzed. The proposed LN-CNN structure can realize both A and B templates with more than two neighborhood layers without complex direct connections between neural cell and neighboring cells. In both A and B templates, the first layer is defined by 4 neighboring cells located at the 4 corners of a diamond shape whereas the second layer is defined by 8 cells. In realizing the 12 template coefficients of a template, only 8 connections are required as compared to 12 connections in the conventional CNN structure. Thus the required chip area for synaptic connection can be reduced. Using the proposed LN-CNN structure, the LN-CNN functions, such as noise removing, Muller-Layer arrowhead illusion, and connected component detection, have been successfully realized and verified in MATLAB simulations. The constraints on the realized templates and the template coefficients in the third or higher layers are analyzed and discussed. Based upon he above successful simulation results, the application of the proposed LN-CNN structure to the design of LN-CNN Universal Machine (LN-CNNUM) is quite feasible. The related research will be conducted in the future.