臺灣博碩士論文加值系統

Many studies have found that High-order neural network (HONN) is enables to boost neural network performance. This research utilize a hybrid model with HONN and Linear Neural Network (NN) concepts to develop high-order and linear neural connectors for layer connections. Consequently, this developed HNN will involve a linear/nonlinear switch for each neural layer connection. Furthermore, fuzzy logic (FL) has already been introduced to neural network and it is also found that the combination of FL and FNN has been proof-reading. Furthermore, fuzzy logic (FL) also has been introduced to neural network and been proofed with fuzzy neural network (FNN). Therefore, this research fuses fuzzy logic additionally to develop a fuzzy hybrid neural network (FHNN) architecture. Sequentially, genetic algorithm (GA) is employed to globally optimize membership function of FL and HNN topology and parameters.
The fundamental of this developed model is a FHNN together with genetic optimization to develop the proposed evolutionary fuzzy hybrid neural networks (EFHNN). EFHNN is capable of handling complexity such as fuzzy/uncertain tasks, linear/nonlinear neural mapping and global optimization. Furthermore, in order to enable to process the EFHNN automatic adaptation, this research work integrates the EFHNN with object-oriented (OO) computer technique which consist of three modules: management module, adaptation module and inference module. The developed system was named as evolutionary fuzzy hybrid neural inference system (EFHNIS).
The main focus of this research will be the optimum linear/nonlinear combinations for neural layers, which is the basis of the proposed hybrid neural network as well as the validation of the proposed EFHNN which collaborated with NN, HONN, FL, and GA concepts. In addition, since the construction managements might have certain issues which are complex and full of uncertain, implementing EFHNN in this topic is proved to be suitable and applicable to reliably assist the decision making in construction industry.

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ABSTRACT i
ACKNOWLEDGEMENTS iii
ABBREVIATIONS AND SYMBOLS ix
LIST OF FIGURES xv
LIST OF TABLES xix

1. INTRODUCTION 1
1.1 Research Motivation 1
1.2 Research Objectives 3
1.3 Scope Definition 4
1.3.1 Scope Identification 4
1.3.2 Research Hypotheses and Assumptions 4
1.4 Research Methodology 6
1.4.1 Problem Formulation 8
1.4.2 Literature Review 8
1.4.3 Model Construction 9
1.4.4 System Development 10
1.4.5 Assessment 11
1.5 Study Outline 11
2. GENETIC ALGORITHM (GA), FUZZY LOGIC (FL), HYBRID NEURAL NETWORK (HNN), EFNIM AND OBJECT-ORIENTED SYSTEM DEVELOPMENT (OOSD) 13
2.1 Genetic Algorithm (GA) 14
2.1.1 Basic Concept 14
2.1.2 Applications in Construction Management 17
2.1.3 Advantages and Disadvantages 18
2.2 Fuzzy Logic (FL) 19
2.2.1 Basic Concept 19
2.2.2 Applications in Construction Management 21
2.2.3 Advantages and Disadvantages 22
2.3 Neural Network (NN) and High Order Neural Network (HONN) 23
2.3.1 Neural Network (NN) Basic Concept 23
2.3.2 High Order Neural Network (HONN) Basic Concept 26
2.3.3 Applications in Construction Management 27
2.3.4 Advantages and Disadvantages 27
2.4 Evolutionary Fuzzy Neural Inference Model (EFNIM) 29
2.4.1 Basic Concept 29
2.4.2 Applications in Construction Management 30
2.4.3 Advantages and Disadvantages 30
2.5 Object-Oriented System Development (OOSD) 31
2.5.1 Basic Concept 31
2.5.2 Applications in Construction Management 33
2.5.3 Advantages and Disadvantages 34
3. EVOLUTIONARY FUZZY HYBRID NEURAL NETWORK (EFHNN) 36
3.1 Hybrid Neural Network Concept 36
3.2 EFHNN Model Architecture 37
3.3 Model Adaptation Process 40
3.3.1 Initialize Population 43
3.3.2 Evaluate Individuals 54
3.3.3 Perform Crossover 65
3.3.4 Perform Mutation 66
3.3.5 Select Individuals 67
3.4 Model Adaptation Example 70
3.4.1 Initialize Population 71
3.4.2 Evaluate Individuals 74
3.4.3 Perform Crossover 82
3.4.4 Perform Mutation 83
3.4.5 Select Individuals 85
3.5 Potential Application Areas 87
3.6 Model Application Process 88
3.7 Model Requirements and Limitations 90
4. EVOLUTIONARY FUZZY HYBRID NEURAL INFERENCE SYSTEM (EFHNIS) 91
4.1 Object-Oriented System Development Process 91
4.1.1 Planning Phase 93
4.1.2 Building Phase 94
4.1.3 Deploying Phase 108
4.2 System Demonstration 111
4.2.1 System Main Form 112
4.2.2 Management Module 112
4.2.3 Adaptation Module 114
4.2.4 Phenotype Module 116
4.2.5 Inference Module 117
5. MODEL VALIDATION 119
5.1 Preparations for Model Validation 120
5.1.1 Data Preprocessing 120
5.1.2 Configuration of Model Parameters 122
5.1.3 General Parameter Settings for Case Studies 124
5.1.4 Performance Evaluation 125
5.2 Exclusive-Or (XOR) 126
5.2.1 Problem Statement 126
5.2.2 Model Application 126
5.2.3 Study of Model Complexity 127
5.3 Function Approximation 128
5.3.1 Problem Statement 128
5.3.2 Model Application 128
5.4 Performance Prediction of Subcontractor 131
5.4.1 Problem Statement 131
5.4.2 Model Application 132
5.5 Dynamic Prediction of Project Sucess 137
5.5.1 Problem Statement 137
5.5.2 Model Application 137
5.6 Conceptual Estimating of Building Cost 143
5.6.1 Problem Statement 143
5.6.2 Model Application 144
5.7 Strategic Control over Project Cash Flows 154
5.7.1 Problem Statement 154
5.7.2 Model Application 156
5.7.3 Study of data impact 165
5.7 Discussions 168
5.7.1 Implementation of EFHNN 164
6. CONCLUSIONS AND RECOMMENDATIONS 170
6.1 Review of Research Objectives 170
6.2 Summary 170
6.3 Conclusions 172
6.4 Research Contributions 174
6.5 Lessons Learned 175
6.6 Recommendations and Future Prospects 176
BIBLIOGRAPHY 179
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