Robust training algorithm for a perceptron neuron

Our interest in this paper is to study the behavior of the perceptron neuron in the presence of disturbance which is always important for practical applications. A robust classifier is required to be insensitive to disturbances and to classify noisy input patterns into the correct class, to which the respective desired input pattern belongs. The projection algorithm with a dead zone is well known in system identification and adaptive control systems to guarantee the convergence. In this paper, the dead zone scheme is used to train the nonlinear perceptron neuron. The trained perceptron neuron is capable of classifying the noisy input pattern sequence into the correct class in the presence of disturbance