Accuracy effects in pattern recognition neural nets

Various errors, including analog accuracy, nonlinearities, and noise, are present in all neural networks. The author considers their effects in training and testing on two different pattern recognition neural nets. He shows that the neural nets considered allow some such effects to be included inherently in the neural net synthesis algorithm and that the effect of the other error sources can be trained out by proper selection of neural net design parameters. Multiclass distortion-invariant pattern recognition neural nets are considered. The results are applicable to analog VLSI and optical neural nets