Initialisation improvement in engineering feedforward ANN models.

Abstract

Any feedforward artificial neural network (ANN) training procedure begins with the initialisation of the connection weights ’ values. These initial values are generally selected in a random or quasi-random way in order to increase training speed. Nevertheless, it is common practice to initialize the same ANN architecture in a repetitive way in order for satisfactory training results to be achieved. This is due to the fact that the error function may have many local extrema and the training algorithm can get trapped in any one of them depending on its starting point based on the particular initialisation of weights. This paper proposes a systematic way for weight initialisation that is based on performing multiple linear regression on the training data. Experimental data from a metal cutting process were used for ANN model building to demonstrate an improvement on both training speed and achieved training error regardless of the selected architecture