by Hau-san Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [178-183]). / Chapter 1 --- INTRODUCTION / Chapter 1.1 --- Learning versus Explicit Programming --- p.1-1 / Chapter 1.2 --- Artificial Neural Networks --- p.1-2 / Chapter 1.3 --- Learning in ANN --- p.1-3 / Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5 / Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7 / Chapter 1.6 --- Incremental Learning --- p.1-10 / Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11 / Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORK / Chapter 2.1 --- The Perceptron --- p.2-1 / Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4 / Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5 / Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEM / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5 / Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9 / Chapter 3.4 --- Concluding Remarks --- p.3-15 / Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKS / Chapter 4.1 --- Introduction --- p.4-2 / Chapter 4.2 --- The Radial Basis Function --- p.4-6 / Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9 / Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11 / Chapter 4.5 --- Initialization of the First Node --- p.4-15 / Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18 / Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20 / Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21 / Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21 / Chapter 4.10 --- Concluding Remarks --- p.4-33 / Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKS / Chapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1 / Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2 / Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3 / Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6 / Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10 / Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12 / Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKS / Chapter 6.1 --- Introduction --- p.6-1 / Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3 / Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5 / Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10 / Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden Nodes / Chapter 6.5.1 --- Introduction --- p.6-12 / Chapter 6.5.2 --- Determination of the Target T-Vector / Chapter 6.5.2.1 --- Introduction --- p.6-15 / Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16 / Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18 / Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24 / Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28 / Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29 / Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30 / Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30 / Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32 / Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35 / Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36 / Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36 / Chapter 6.11 --- Concluding Remarks --- p.6-50 / Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEME / Chapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1 / Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2 / Chapter 7.3 --- The Generalization Measure --- p.7-4 / Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5 / Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6 / Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8 / Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12 / Chapter 7.8 --- Concluding Remarks --- p.7-16 / Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZE / Chapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1 / Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2 / Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6 / Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6 / Chapter 8.5 --- Concluding Remarks --- p.8-16 / Chapter 9 --- CONCLUSION / Chapter 9.1 --- Contributions --- p.9-1 / Chapter 9.2 --- Suggestions for Further Research --- p.9-3 / REFERENCES --- p.R-1 / APPENDIX --- p.A-1
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_319198 |
| Date | January 1993 |
| Contributors | Wong, Hau-san., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Publisher | Chinese University of Hong Kong |
| Source Sets | The Chinese University of Hong Kong |
| Language | English |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, ix, [185] leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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