Neural Networks with Hybrid Morphological / Rank / Linear
نویسنده
چکیده
We propose a general class of multilayer feed-forward neural networks where the combination of inputs in every node is formed by hybrid linear and nonlinear (of the morphological/rank type) operations. We demonstrate that this structure ooers eecient solutions to pattern classiication problems by requiring fewer nodes or fewer parameters to estimate than those needed by multilayer perceptrons. It can also solve the parity problem in closed-form. For its design we formulate a methodology using ideas from the back-propagation algorithm and robust techniques to circumvent the non-diierentiability of rank functions.
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