Performance of ANN in Pattern Recognition For Process Improvement Using Levenberg- Marquardt And Quasi-Newton Algorithms

In Industrial manufacturing, Quality has become one of the most important consumer decision factors in the selection among competing products and services. Product inspection is an important step in the production process. Since product reliability is most important in mass production facilities. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data. Neural networks are being successfully applied across a wide range of application domains in business, medicine, geology and physics to solve problems of prediction, classification and control. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition for process improvement using two selected training algorithms (Levenberg- Marquardt and Quasi-Newton Algorithm). The result of this paper clearly indicates that L-M algorithm has fastest network convergence rate than Q-N algorithm in production process