An Identification of Faults in Industries Using Hybrid Feed Forward Neural Network – Particle Swarm Optimization

In order to enhance the quality governance in automotive firms the fault analysis and categorization is designed with optimal image renewals employing swarm intelligence scheme with improved precision classifier. Method: ology: Preliminarily the accumulated information is preprocessed for eradicating the undesirable noise and renewal is achieved employing non – local means scheme, followed by which five characteristics like arithmetic mean, variance, standard deviation, skewness and auto correlation are mined. The mined characteristics are sent to the feed forward neural network (FFNN) classifier for recognizing faults in the computerized segments produced in the firms. In FFNN the particle swarm optimization (PSO) is employed to optimize the characteristics for effective fault identification in metal sheets. Results: : The experimental analysis reveals that the designed FFNN – PSO scheme acquires improved performance with increased rate of accuracy of 92.86%, sensitivity rate of 95.24%, specificity rate of 90.48%, G – mean rate of 97.47% and precision rate of 90.90% evaluated against the prevailing classifiers.