Boltzmann learning of parameters in cellular neural networks

Abstract

The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imager