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研究生： 黎中旦
Le - Trung Dan
論文名稱： Enhanced Time-Dependent Evolutionary Fuzzy Support Vector Machine Inference Model for Cash-Flow Prediction and Estimate at Completion
Enhanced Time-Dependent Evolutionary Fuzzy Support Vector Machine Inference Model for Cash-Flow Prediction and Estimate at Completion
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 周瑞生
Jui-Sheng Chou
潘南飛
Nang-Fei Pan
鄭道明
T-M, Cheng
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 110
中文關鍵詞： Time series 、Fuzzy Logic 、Weighted Support Vector Machines 、Fast Messy Genetic Algorithms 、Cash flow Prediction 、Estimate at Completion
外文關鍵詞： Time series, Fuzzy Logic, Weighted Support Vector Machines, Fast Messy Genetic Algorithms, Cash flow Prediction, Estimate at Completion
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    • This study has a two-fold objective. First, it conducts a mechanism enhance time series data of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model is called EFSIMET. The EFSIMET was developed particularly to treat construction management problems that contain time series data. The EFSIMET¬ is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the author develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has the capacity to address the complex and dynamic nature of time series data; thus, it could improve the model’s performance significantly.
    Nowadays, construction management is facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash-flow and estimate at completion. Through performance’s comparison with previous works, the effectiveness and real world application of EFSIMET are proved. Hence, this model may be use as an intelligent decision support tool to assist the decision-making process to solve the construction management’s difficulties.

    This study has a two-fold objective. First, it conducts a mechanism enhance time series data of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model is called EFSIMET. The EFSIMET was developed particularly to treat construction management problems that contain time series data. The EFSIMET¬ is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the author develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has the capacity to address the complex and dynamic nature of time series data; thus, it could improve the model’s performance significantly.
    Nowadays, construction management is facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash-flow and estimate at completion. Through performance’s comparison with previous works, the effectiveness and real world application of EFSIMET are proved. Hence, this model may be use as an intelligent decision support tool to assist the decision-making process to solve the construction management’s difficulties.

    ABSTRACT I ACKNOWLEGDEMENT II TABLE OF CONTENTS III ABBREVIATIONS AND SYMBOLS VI LIST OF FIGURES XI LIST OF TABLES XIII CHAPTER 1: INTRODUCTION 1 1.1 RESEARCH MOTIVATION 1 1.2 RESEARCH OBJECTIVE 4 1.3 DETERMINE SCOPE OF STUDY 5 1.3.1 Boundary determination 5 1.3.2 Key assumptions 5 1.4 RESEARCH METHODOLOGY 6 1.4.1 Identify the problems 8 1.4.2 Review of Literature 8 1.4.3 Model Establishment 9 1.4.4 Model Validation and Application 9 1.5 STUDY OUTLINE 10 CHAPTER 2: LITERATURE REVIEW 11 2.1 TIME SERIES ANALYSIS 11 2.2 FUZZY LOGIC (FL) 14 2.3 WEIGHTED SUPPORT VECTOR MACHINE 17 2.4 FAST MESSY GENETIC ALGORITHM 20 CHAPTER 3: ENHANCED TIME - DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL (EFSIMET) 24 3.1 MODEL ARCHITECTURE 24 3.2 MODEL ADAPTATION PROCESS 25 3.2.1 Initialize competitive template 27 3.2.2 Initial phase 30 3.2.3 Primordial phase 37 3.2.4 Juxtapositional phase 38 3.3 TERMINATION CRITERION 39 CHAPTER 4: PREDICT PROJECT CASH-FLOW USING EFSIMET 40 4.1 CASH FLOW IN CONSTRUCTION PROJECT 40 4.2 MODEL APPLICATION PROCESS 41 4.2.1 Study Case Feasibility 42 4.2.2 Data Collection 42 4.2.3 Data Processing and Identify influencing factors (attributes) 42 4.2.4 Parameters configuration 44 4.3 CASH-FLOW PREDICTION RESULT 44 4.3.1 Cash-flow Prediction Result Using EFSIMT 45 4.3.2 Cash-Flow Prediction Result Using EFSIMET 51 4.3.3 Result Comparison 53 CHAPTER 5: ESTIMATE AT COMPLETION USING EFSIMET 56 5.1 ESTIMATE AT COMPLETION 56 5.2 CONSTRUCTING EAC PREDICTION MODEL USING EFSIMET 59 5.2.1 Parameters configuration 63 5.2.2 Model training 63 5.2.3 Model testing 63 5.3 EAC PREDICTION RESULT USING EFSIMT 64 5.3.1 Testing result of dataset L 66 5.3.1 Testing result of dataset M 70 5.3.2 Summarize the testing result of using EFSIMT 73 5.4 EAC PREDICTION RESULT USING EFSIMET 73 5.4.1 Testing Result of Dataset L 74 5.4.2 Testing Result of Dataset M 76 5.4.3 Result Comparison 78 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 82 6.1 REVIEW THE RESEARCH OBJECTIVE 82 6.2 SUMMARY 82 6.3 CONCLUSIONS 83 6.4 RECOMMENDATION FOR FURTHER STUDY 84 REFERENCES 86

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