Wavelet-cellular neural network architecture and learning algorithm

Abdullah Bal; Osman Nuri Ucan; Halit Pastaci; Mohammad S. Alam

doi:10.1117/12.542353

12 April 2004 Wavelet-cellular neural network architecture and learning algorithm

Abdullah Bal, Osman Nuri Ucan, Halit Pastaci, Mohammad S. Alam

Proceedings Volume 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II; (2004) https://doi.org/10.1117/12.542353
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Cellular Neural Networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.

Citation Download Citation

Abdullah Bal, Osman Nuri Ucan, Halit Pastaci, and Mohammad S. Alam "Wavelet-cellular neural network architecture and learning algorithm", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); https://doi.org/10.1117/12.542353

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