Role of Hidden Neurons in an Elman Recurrent Neural Network in Classification of Cavitation Signals

Abstract

This paper is intended to present the outcome of a study conducted on the cavitation data collected from accelerometer which is installed at the down stream of the cavitation test loop, to illustrate that the hidden neurons in an ANN modelling tool, indeed, do have roles to play in percentage of classification of cavitation signal. It sheds light on the role of the hidden neurons in an Elman Recurrent type ANN model which is used to classify the cavitation signals. The results confirmed that the hidden-output connection weights become small as the number of hidden neurons becomes large and also that the trade-off in the learning stability between input-hidden and hidden-output connections exists. The Elman recurrent network propagates data from later processing stage to earlier stage. A copy of the previous values of the hidden units are maintained which allows the network to perform sequence-prediction. In the present work, the optimum number of hidden neurons is evolved through an elaborate trial and error procedure. It is concluded that our approach has a significant improvement in learning and also in classification of cavitation signals. General Terms Optimum ANN model development.