Function approximation using neural networks : a simulation study

Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144-148)Microfiche.xiii, 148 leaves, bound ill. 29 cmThis dissertation analyzes the effect of different characteristics of data on the training and estimation accuracy of neural networks. The literature on the universal approximation property of neural networks is reviewed. An examination of the relationship of the neural network approach to traditional statistical methods of approximation brought about proposed enhancements to the neural network training procedure. The study generated data samples characterized by different functional forms, levels of random noise, number and magnitude of outliers, and strength of multicollinearity. These samples were then used to train a neural network. The accuracy of the neural network estimate was tested and compared with the accuracy of the estimates obtained from the true model and those from Specht's GRNN model. Statistics on the length of training and the complexity of the neural network estimate were also collected and analyzed