Abstract
The area of artificial neural networks has recently seen an explosion of theoretical and practical results. In this paper, we present an artificial neural network that is algebraically distinct from the classical artificial neural networks, and several applications which are different from the typical ones. In fact, this new class of networks, calledmorphology neural networks, is a special case of a general theory of artificial neural nets, which includes the classical neural nets. The main difference between a classical neural net and a morphology neural net lies in the way each node algebraically combines the numerical information. Each node in a classical neural net combines information by multiplying output values and corresponding weights and summing, while in a morphology neural net, the combining operation consists of adding values and corresponding weights, and taking the maximum value. We lay a theoretical foundation for morphology neural nets, describe their roots, and give several applications in image processing. In addition, theoretical results on the convergence issues for two networks are presented.
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This research was supported in part by National Science Foundation, Contract No. ECS-9010403.
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Davidson, J.L., Hummer, F. Morphology neural networks: An introduction with applications. Circuits Systems and Signal Process 12, 177–210 (1993). https://doi.org/10.1007/BF01189873
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DOI: https://doi.org/10.1007/BF01189873