A Boolean function generator with learning capability

Abstract

The authors use a neural technique to implement a positive logic Boolean function or truth table. The neural technique is a perceptron training algorithm by which a Boolean function or truth table can be generated. The connected weight value in the neural network represents the sum of product terms of a Boolean function or row vectors of a truth table. A neural technique for generating functional-link cells for successful learning is described. The authors then provide an improved algorithm to describe the successful learning steps to generate the logic function and then present examples to illustrate these learning steps. Finally, a function diagram is specified to illustrate the overall system function. >