Neural network classifier with analytic translation and scaling capabilities for optimal signal viewing

摘要

A neural network originally proposed by Szu for performing pattern recognition has been modified for use in a noisy manufacturing environment. The network in this paper has the capability to analytically translate and scale its internal representation of the signal so that it overlays the presented signal. A response surface in the neighborhood of the stored reference signal is built during training and covers the range of translate and scale parameter values expected. A genetic algorithm is used to search over this hilly terrain to find the optimal values of these parameters so that the reference signal overlays the presented signal. The procedure is repeated over all hypothesized pattern classes with the best fit identifying the class.