An introduction to neural networks based on the feed forward, backpropagation error correction network with weight space limiting based on a priori knowledge

Neural networks are introduced to instrumentation professionals. The structure of neural networks is described with particular attention paid to the backpropagation network. Both graphic and analytical descriptions are used. Examples of backpropagation networks applied to one- and two-dimensional resolution enhancement are used to exhibit characteristics of there networks. In the two-dimensional case, image recovery and enhancement of Hubble-space-telescope-like images are employed as examples. Several approaches to the effective limitation of the network weight space are reported. The conceptual basis of weight space limitation is introduced. The connection of weight space limitation to incorporation of a priori knowledge of the systems to which the networks are applied is discussed with examples