Algorithm for Pulse Coupled Neural Network parameters estimation

The paper introduces an approach for linking and decay coefficient estimation in the Optimized Pulse Coupled Neural Network with Modified feeding input (OM-PCNN). These two parameters have significant importance to improve the image recognition precision. The standard PCNN has ten parameters, so it is very difficult to find optimal closed system of all its parameters. By introducing the OM-PCNN it is needed to set up four parameters only — linking coefficient, decay coefficient of threshold potential, linking radius and PCNN kernel. The rest two parameters of OM-PCNN are adjusted by rule of computational complexity minimization.

I. Introduction

The application spectrum of PCNN [1] usage is extended to all areas of image processing nowadays. The PCNN was used mainly in image segmentation, noise filtering, feature generation for image recognition, edge and object detection in the image and also in image fusion process. There are also known cases of PCNN application in the speech recognition area. The present research is oriented mainly to optimization of PCNN mathematical model and its parameters.