Use of the Circle-Segments Method as a Data Visualization Tool for an Artificial Neural Network
Shir Li, Wang Chee, P.H. Lim, Zalina Abdul Aziz · 2006
Process modeling and prediction is one of the major tasks in many industrial applications. The Multi-Layered Perceptron (MLP) neural network has been a popular approach in process modeling and prediction, and has produced good results. One of the disadvantages of the MLP is that it is unable to provide a visualization effect of the underlying relationships between the input and output data. In this paper, we propose to use the circle-segments method as a visualization tool for the MLP. The applicability of the hybrid MLP and circle-segments approach is demonstrated using a case study on a closed-loop disk drive head system. The performance is compared with that from the Response Surface Methodology (RSM). From the results obtained, the MLP network shows a better prediction capability as compared with the RSM. In terms of visualization, the circle-segments method is able to overcome the limitations of contour plots in RSM in disclosing the relationships of the input-output data. Keywords Artificial neural networks, Multi-Layer Perceptron, circle segments, design of experiments, data visualization 1.