DOI: 10.7763/IJCTE.2011.V3.288
Statistical Normalization and Back Propagationfor Classification
Abstract—The artificial neural network has recently been applied in many areas of medical and medically related fields. It is known as an excellent classifier of nonlinear input and output numerical data. Some major issues are to be considered before using the neural network models, such as the network structure, learning rate parameter, and normalization methods for the input vectors. The proposed research showed various normalization methods used in back propagation neural networks to enhance the reliability of the trained network. The experimental results showed that the performance of the diabetes data classification model using the neural networks was dependent on the normalization methods.
Index Terms—Artificial Neural Networks; Back Propagation; Diabetes Mellitus; Normalization.
T. Jayalakshmi is with the CMS College of Science and Commerce, Coimbatore, India (Phone: 9976268841; e-mail: jayas20@ rediffmail.com).
A. Santhakumaran., is with Salem Sowdeswari College, Salem, India. (e-mail: ask.stat@yahoo.com).
Cite: T. Jayalakshmi, A. Santhakumaran, "Statistical Normalization and Back Propagation for Classification," International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 89-93, 2011.