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ABSTRACT
A new learning algorithm for space invariant Cellular Neural Network (CNN) is introduced. Learning is formulated as an optimization problem by combining rough sets and genetic programming. Rough Sets approach has been selected for creating priori knowledge about the actual effective cells, determining their significance in classifying the output, and discovering the optimal CNN structure. According to the lattice of CNN architecture and depending on the priori knowledge gained by rough sets, genetic programming will be used in deriving the cloning template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.