Function approximation in high-dimensional spaces using lower-dimensional Gaussian RBF networks.

by Jones Chui.Thesis (M.Phil.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves 62-[66]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Fundamentals of Artificial Neural Networks --- p.2Chapter 1.1.1 --- Processing Unit --- p.2Chapter 1.1.2 --- Topology --- p.3Chapter 1.1.3 --- Learning Rules --- p.4Chapter 1.2 --- Overview of Various Neural Network Models --- p.6Chapter 1.3 --- Introduction to the Radial Basis Function Networks (RBFs) --- p.8Chapter 1.3.1 --- Historical Development --- p.9Chapter 1.3.2 --- Some Intrinsic Problems --- p.9Chapter 1.4 --- Objective of the Thesis --- p.10Chapter 2 --- Low-dimensional Gaussian RBF networks (LowD RBFs) --- p.13Chapter 2.1 --- Architecture of LowD RBF Networks --- p.13Chapter 2.1.1 --- Network Structure --- p.13Chapter 2.1.2 --- Learning Rules --- p.17Chapter 2.2 --- Construction of LowD RBF Networks --- p.19Chapter 2.2.1 --- Growing Heuristic --- p.19Chapter 2.2.2 --- Pruning Heuristic --- p.27Chapter 2.2.3 --- Summary --- p.31Chapter 3 --- Application examples --- p.34Chapter 3.1 --- Chaotic Time Series Prediction --- p.35Chapter 3.1.1 --- Performance Comparison --- p.39Chapter 3.1.2 --- Sensitivity Analysis of MSE THRESHOLDS --- p.41Chapter 3.1.3 --- Effects of Increased Embedding Dimension --- p.41Chapter 3.1.4 --- Comparison with Tree-Structured Network --- p.46Chapter 3.1.5 --- Overfitting Problem --- p.46Chapter 3.2 --- Nonlinear prediction of speech signal --- p.49Chapter 3.2.1 --- Comparison with Linear Predictive Coding (LPC) --- p.54Chapter 3.2.2 --- Performance Test in Noisy Conditions --- p.55Chapter 3.2.3 --- Iterated Prediction of Speech --- p.59Chapter 4 --- Conclusion --- p.60Chapter 4.1 --- Discussions --- p.60Chapter 4.2 --- Limitations and Suggestions for Further Research --- p.61Bibliography --- p.6