New algorithm to desing multiple hiden layer artificial neural networks

Abstract

This thesis presents a new constructive algorithm called multilayered constructive architecture (MCA) for designing and training multiple hidden layered artificial neural networks (ANNs). Unlike most previous constructive algorithms, MCA puts emphasis on both simplicity and generalization ability of designed ANNs. In order to maintain simplicity, MeA uses a minimum number of user specified parameters in designing ANNs. The use of both layered and cascaded architecture in MCA increases the generalization ability of designed ANNs. MCA has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass and heart disease. The experimental results show that MCA can produce compact ANNs with good generalization ability.