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論文中文名稱:應用演化式計算為基之幅狀基底函數神經網路於工業電腦銷售量預測之研究 [以論文名稱查詢館藏系統]
論文英文名稱:Application of Evolutionary Computation-Based Radial Basis Function Neural Network to IPC Sales Forecasting [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:工商管理研究所
畢業學年度:98
出版年度:99
中文姓名:陳振耀
英文姓名:Zhen-Yao Chen
研究生學號:94749003
學位類別:博士
語文別:英文
口試日期:2010-06-30
論文頁數:96
指導教授中文名:郭人介 教授;胡同來 教授
指導教授英文名:Ren-Jieh Kuo;Tung-Lai Hu
口試委員中文名:田方治 教授;王福琨 教授;蔣明晃 教授;吳泰熙 教授;許鉅秉 教授
口試委員英文名:Fang-Chih Tien;Fu-Kwun Wang;David Ming-Huang Chiang;Tai-Hsi Wu;Jiuh-Bing Sheu
中文關鍵詞:幅狀基底函數神經網路基因演算法粒子群最佳化類免疫系統演化式計算為基之演算法工業電腦銷售預測
英文關鍵詞:Radial basis function neural networkGenetic algorithmParticle swarm optimizationArtificial immune systemEvolutionary computation-based algorithmIndustrial personal computerSales forecasting
論文中文摘要:由於預測在實務應用上可確保產能與適恰存貨量的有效配置,其的確是個至關重要的因素。而在採取線性預測方式的自廻歸整合移動平均式模型中,會受到實體世界於預測複雜資料問題上的限制,致使有其他的一些方法被發展出來並克服了在非線性預測上的挑戰。因此,本研究即為了預測非線性資料而欲發展出三個以整合性演化式計算為基的演算法,藉此來訓練幅狀基底函數神經網路。這些以演化式計算為基的演算法包括:基因演算法、粒子群最佳化和類免疫系統。

有三個標竿的連續性測試函數被用來驗證所發展出的這三個以整合性演化式計算為基的演算法,其實驗上的結果著實亦大有可為。此外,藉由臺灣的一家國際知名工業電腦製造商所提供的工業電腦銷售資料,也被進一步的用以評估這些所發展出的演算法。而模型評估結果指出,其所發展的演算法確實能預測得更為準確。更有甚者,若再考慮國際匯兌要素,便可改善其預測結果。
論文英文摘要:Forecasting is one of the crucial factors in practical application since it ensures the effective allocation of capacity and proper amount of inventory. Since auto-regressive integrated moving average (ARIMA) models which are more suitable for linear data have their constraints in predicting complex data for the real-world problems, some approaches have been developed to conquer the challenge of nonlinear forecasting. Therefore, for the purpose of forecasting nonlinear data, this study intends to develop three integrated evolutionary computation (EC)-based algorithms for training radial basis function neural network (RBFnn). The EC-based algorithms include genetic algorithm (GA), particle swarm optimization (PSO), and artificial immune system (AIS).

In order to verify these three developed integrated EC-based algorithms, three benchmark continuous test functions were employed. The experimental results of three integrated EC-based algorithms are really very promising. In addition, industrial personal computer (IPC) sales data provided by an international well-known IPC manufacturer in Taiwan is also applied to further assess these developed algorithms. The model evaluation results indicated that the developed algorithms really can forecast more accurately. Furthermore, if foreign exchange (FX) factor is considered, the forecasting results can be improved.
論文目次:摘要 i
ABSTRACT ii
誌謝 iii
TABLE OF CONTENTS vii
LIST OF TABLES ix
LIST OF FIGURES x
Chapter 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Research Scope 3
1.4 Research Assumptions 4
1.5 Research Limitations 4
1.6 Research Architecture 5
Chapter 2 LITERATURE REVIEW 6
2.1 Forecasting 6
2.2 Neural Networks (NNs) 7
2.3 Radial Basis Function Neural Network (RBFnn) 9
2.4 Evolutionary Computation (EC)-Based algorithm for RBFnn Learning 11
2.4.1 Genetic Algorithm (GA)-Based RBFnn Algorithm 12
2.4.2 Particle Swarm Optimization (PSO)-Based RBFnn Algorithm 14
2.4.3 Artificial Immune System (AIS)-Based RBFnn Algorithm 17
2.5 Integration of EC-based Algorithms for RBFnn Learning 20
2.5.1 Integration of GA and PSO Algorithms 21
2.5.2 Integration of AIS and GA Algorithms 23
2.5.3 Integration of AIS and PSO Algorithms 25
Chapter 3 METHODOLOGY 26
3.1 EC-Based Algorithm 26
3.2 Integration of GA and PSO Algorithms for RBFnn Learning 28
3.3 Integration of AIS and GA Algorithms for RBFnn Learning 39
3.4 Integration of AIS and PSO Algorithms for RBFnn Learning 44
Chapter 4 EXPERIMENT RESULTS 50
4.1 Benchmark Continuous Test Functions 51
4.2 Parameter Setup 51
4.2.1 Parameters Setup for the IPGO Algorithm 52
4.2.2 Parameters Setup for the IAG Algorithm 53
4.2.3 Parameters Setup for the IIP Algorithm 54
4.3 Performance Evaluation and Comparison 54
Chapter 5 CASE STUDY 61
5.1 The Practical Case for Sales Forecasting 61
5.2 Building ARIMA Models 63
5.3 Data Encoding for Sales Forecasting 64
5.4 Performance Measurement for Sales Forecasting 65
5.5 Parameters Setup for the Sales Forecasting Case 66
5.6 The Analysis and Comparison of Forecasting Results 73
Chapter 6 CONCLUSIONS AND SUGGESTIONS 76
6.1 Conclusions 76
6.2 Contributions 76
6.3 Future Studies 77
REFERENCES 78
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