Fault Diagnosis Competitive Neural Network Training Through Condition Monitoring of Industrial Machines and Stock Exchange Prediction

In this paper a new way for neural network training is introduced where the output of middle (hidden) layer of neural network is used to update weights in a competition procedure. Output layer’s weights are modified with multi layer perceptron (MLP) policy. This learning method is applied to two systems as case studies. First one is the monitoring of industrial machine where the results are compared with other training methods such as MLP or Radial Basis Function (RBF). Oil analysis data is used for condition monitoring. The data is gathered by using ten stages technique. The second one is the Stock prediction where the data are highly nonlinear and normally unpredictable especially when the markets are affected by political facts. The simulation results are analyzed and compared with other methods.