臺灣博碩士論文加值系統

This research proposes two-stage method, growing self-organizing map (GSOM) algorithm and bee colony optimization (BCO) based self-organizing map (BSOSOM), to improve SOM performance. In the first stage, GSOM is used to determine the SOM topology and then followed by BCOSOM to fine tune the SOM weights. The proposed BCOSOM algorithm is compared with other algorithms, PSO, BCO, SOM, PSOSOM, SOM+PSO, and SOM+BCO, using four benchmark data sets, Iris, Glass, Wine, and Vowel. The computational result indicates that BCOSOM algorithm is able to find a better solution than other algorithms. Furthermore, the proposed algorithm has been also employed to Group Technology to cluster components into part families for a medical manufacture in Indonesia

This research proposes two-stage method, growing self-organizing map (GSOM) algorithm and bee colony optimization (BCO) based self-organizing map (BSOSOM), to improve SOM performance. In the first stage, GSOM is used to determine the SOM topology and then followed by BCOSOM to fine tune the SOM weights. The proposed BCOSOM algorithm is compared with other algorithms, PSO, BCO, SOM, PSOSOM, SOM+PSO, and SOM+BCO, using four benchmark data sets, Iris, Glass, Wine, and Vowel. The computational result indicates that BCOSOM algorithm is able to find a better solution than other algorithms. Furthermore, the proposed algorithm has been also employed to Group Technology to cluster components into part families for a medical manufacture in Indonesia

ABSTRACT 2
ACKNOWLEDGEMENTS 3
CONTENTS 4
LIST OF FIGURES 6
LIST OF TABLES 7
Chapter 1 INTRODUCTION 9
1.1 Research Background 9
1.2 Research Objectives 10
1.3 Research Scope and Constrain 10
1.4 Research Framework 10
Chapter 2 LITERATURE REVIEW 12
2.1 Self-organizing Maps (SOM) 12
2.1.1 Basic concept of SOM 12
2.1.2 Original Algorithm of SOM 13
2.2 Growing Self-organizing Maps (GSOM) 13
2.2.1 Basic Concept of GSOM 13
2.3 Particle Swarm Optimization (PSO) 16
2.3.1 Basic Concept of PSO 16
2.3.2 PSO Algorithm 16
2.4 Bee Colony Optimization (BCO) 17
Chapter 3 METHODOLOGY 19
3.1 First Stage-GSOM 20
3.2 Second Stage 21
3.2.1 Bee Colony Optimization (BCO) 21
3.2.2 BCOSOM Algorithm 22
3.2.3 SOM+BCO algorithm 23
Chapter 4 COMPUTATIONAL RESULTS 25
4.1 Parameter setup 25
4.2 Computational result 31
4.2.1 First stage 31
4.2.2 Second stage 33
4.3 Statistical Test 34
4.4 Algorithm Convergence 35
Chapter 5 MODEL EVALUATION RESULTS 38
5.1 Source of practical case and data processing 38
5.2 The result and analysis 40
5.2.1 SOM topology determination using GSOM algorithm 40
5.2.2 Comparison of proposed algorithm 40
Chapter 6 CONCLUSION AND FUTURE RESEARCH 44
6.1 Conclusions 44
6.2 Contributions 44
6.3 Future Research 44
REFERENCES 46
Appendix 1 48
Appendix II 53

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