On CNN universal perceptron

Perceptron is one of the most important aspects of artificial neural networks (ANN), while cellular neural networks (CNN) are biologically inspired systems in which computation emerges from the collective behavior of some locally coupled simple cells. However, whether the minimal number of the neurons in the hidden layer of a perceptron needed or a CNN template design for performing a prescribed task has not been completely characterized today. This article summarizes several algorithms for decomposing linearly non-separable Boolean function, specially a DNA-like decomposing algorithm and a shortest distance decomposing algorithm, with emphasis on the relationship between universal perceptron (UP) and CNN, and provides some examples to show the powerful ability of these algorithms in decomposing non-LSBF. Moreover, a new concept named CNN-UP is developed, which may lead to a useful new PC software in designing CNN and perceptron in the near future. © 2010 IEEE.