Soft self-organizing map.

Jazyk: angličtina
Rok vydání: 1995
Předmět:
Druh dokumentu: Bibliografie
Popis: by John Pui-fai Sum.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.
Includes bibliographical references (leaves 99-104).
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Motivation --- p.1
Chapter 1.2 --- Idea of SSOM --- p.3
Chapter 1.3 --- Other Approaches --- p.3
Chapter 1.4 --- Contribution of the Thesis --- p.4
Chapter 1.5 --- Outline of Thesis --- p.5
Chapter 2 --- Self-Organizing Map --- p.7
Chapter 2.1 --- Introduction --- p.7
Chapter 2.2 --- Algorithm of SOM --- p.8
Chapter 2.3 --- Illustrative Example --- p.10
Chapter 2.4 --- Property of SOM --- p.14
Chapter 2.4.1 --- Convergence property --- p.14
Chapter 2.4.2 --- Topological Order --- p.15
Chapter 2.4.3 --- Objective Function of SOM --- p.15
Chapter 2.5 --- Conclusion --- p.17
Chapter 3 --- Algorithms for Soft Self-Organizing Map --- p.18
Chapter 3.1 --- Competitive Learning and Soft Competitive Learning --- p.19
Chapter 3.2 --- How does SOM generate ordered map? --- p.21
Chapter 3.3 --- Algorithms of Soft SOM --- p.23
Chapter 3.4 --- Simulation Results --- p.25
Chapter 3.4.1 --- One dimensional map under uniform distribution --- p.25
Chapter 3.4.2 --- One dimensional map under Gaussian distribution --- p.27
Chapter 3.4.3 --- Two dimensional map in a unit square --- p.28
Chapter 3.5 --- Conclusion --- p.30
Chapter 4 --- Application to Uncover Vowel Relationship --- p.31
Chapter 4.1 --- Experiment Set Up --- p.32
Chapter 4.1.1 --- Network structure --- p.32
Chapter 4.1.2 --- Training procedure --- p.32
Chapter 4.1.3 --- Relationship Construction Scheme --- p.34
Chapter 4.2 --- Results --- p.34
Chapter 4.2.1 --- Hidden-unit labeling for SSOM2 --- p.34
Chapter 4.2.2 --- Hidden-unit labeling for SOM --- p.35
Chapter 4.3 --- Conclusion --- p.37
Chapter 5 --- Application to vowel data transmission --- p.42
Chapter 5.1 --- Introduction --- p.42
Chapter 5.2 --- Simulation --- p.45
Chapter 5.2.1 --- Setup --- p.45
Chapter 5.2.2 --- Noise model and demodulation scheme --- p.46
Chapter 5.2.3 --- Performance index --- p.46
Chapter 5.2.4 --- Control experiment: random coding scheme --- p.46
Chapter 5.3 --- Results --- p.47
Chapter 5.3.1 --- Null channel noise (σ = 0) --- p.47
Chapter 5.3.2 --- Small channel noise (0 ≤ σ ≤1) --- p.49
Chapter 5.3.3 --- Large channel noise (1 ≤σ ≤7) --- p.49
Chapter 5.3.4 --- Very large channel noise (σ > 7) --- p.49
Chapter 5.4 --- Conclusion --- p.50
Chapter 6 --- Convergence Analysis --- p.53
Chapter 6.1 --- Kushner and Clark Lemma --- p.53
Chapter 6.2 --- Condition for the Convergence of Jou's Algorithm --- p.54
Chapter 6.3 --- Alternative Proof on the Convergence of Competitive Learning --- p.56
Chapter 6.4 --- Convergence of Soft SOM --- p.58
Chapter 6.5 --- Convergence of SOM --- p.60
Chapter 7 --- Conclusion --- p.61
Chapter 7.1 --- Limitations of SSOM --- p.62
Chapter 7.2 --- Further Research --- p.63
Chapter A --- Proof of Corollary1 --- p.65
Chapter A.l --- Mean Average Update --- p.66
Chapter A.2 --- Case 1: Uniform Distribution --- p.68
Chapter A.3 --- Case 2: Logconcave Distribution --- p.70
Chapter A.4 --- Case 3: Loglinear Distribution --- p.72
Chapter B --- Different Senses of neighborhood --- p.79
Chapter B.l --- Static neighborhood: Kohonen's sense --- p.79
Chapter B.2 --- Dynamic neighborhood --- p.80
Chapter B.2.1 --- Mou-Yeung Definition --- p.80
Chapter B.2.2 --- Martinetz et al. Definition --- p.81
Chapter B.2.3 --- Tsao-Bezdek-Pal Definition --- p.81
Chapter B.3 --- Example --- p.82
Chapter B.4 --- Discussion --- p.84
Chapter C --- Supplementary to Chapter4 --- p.86
Chapter D --- Quadrature Amplitude Modulation --- p.92
Chapter D.l --- Amplitude Modulation --- p.92
Chapter D.2 --- QAM --- p.93
Bibliography --- p.99
Databáze: Networked Digital Library of Theses & Dissertations
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