Universal architectures for logical neural nets

A universal architecture of logical neural nets is proposed which includes the conventional N-tuple and pyramid architectures as its extremes. The discrimination function of the architecture can be adjusted conveniently via the structure parameters and proper spreading operation. This flexibility enables tailored discriminator design in a practical environment. The technique of spreading with a multilayered net is studied and the authors conclude that spreading is only efficient for the first layer. The discussion in this report is concentrated on understanding the different aspects of the neural net as a discriminator. A practical classifier containing several discriminators can be easily designed based on the results here and some global considerations.<
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