论文信息 - Numerical approach to the CNN Based Hole-Filler Template Design Using Leapfrog Method

Numerical approach to the CNN Based Hole-Filler Template Design Using Leapfrog Method

The Cellular Neural Network (CNN) is an artificial neural network of the nearest neighbour interaction type. It has been used for image processing, pattern recognition, moving object detection, signal processing, augmented reality and etc. The cellular neural network CMOS array was implemented by Anguita et al [1 5] and Dalla Betta et al [6]. The design of a cellular neural network template is an important problem, and has received wide attention [7 9]. Based on the dynamic analysis of a cellular neural network, this paper presents, a design method for the template of the hole-filler used to improve the performance of the handwritten character recognition using Leapfrog method. This is done by analyzing the features of the hole-filler template and the dynamic process of CNN and by using single-term Haar wavelet series method (STHW) and Leapfrog methods to obtain a set of inequalities satisfying its output characteristics as well as the parameter range of the hole-filler template. Some simulation results and comparisons are also presented.

[1] L. Chua,et al. An analytic method for designing simple cellular neural networks , 1991 .

[2] Mancia Anguita,et al. VLSI implementations of CNNs for image processing and vision tasks: single and multiple chip approaches , 1996, 1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96).

[3] Leon O. Chua,et al. Cellular neural networks: Theory and circuit design , 1992, Int. J. Circuit Theory Appl..

[4] M. Boroushaki,et al. Simulation of nuclear reactor core kinetics using multilayer 3-D cellular neural networks , 2005, IEEE Transactions on Nuclear Science.

[5] Lin-Bao Yang,et al. Cellular neural networks: theory , 1988 .

[6] Antonio F. Díaz,et al. Parameter Configurations for Hole Extraction in Cellular Neural Networks (CNN) , 2002 .

[7] Mancia Anguita,et al. Focal-Plane and Multiple Chip VLSI Approaches to CNNs , 1998 .

[8] Ignacio Rojas,et al. Area efficient implementations of fixed-template CNN's , 1998 .

[9] Mancia Anguita,et al. A low-power CMOS implementation of programmable CNN's with embedded photosensors , 1997 .

[10] Janez Puhan,et al. A rigorous design method for binary cellular neural networks , 1998, Int. J. Circuit Theory Appl..