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研究生： 馬天王
Mohamad - Aulady
論文名稱： A Hybrid Symbiotic Organisms Search-Quantum Neural Network for Predicting High Performance Concrete Compressive Strength
A Hybrid Symbiotic Organisms Search-Quantum Neural Network for Predicting High Performance Concrete Compressive Strength
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 謝佑明
Yo-Ming Hsieh
郭斯傑
Sy-Jye Guo
蘇振維
Cheng-Wei Su
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： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 112
中文關鍵詞： 預測 、混凝土強度 、共生有機體搜索 - 量子神經網絡
外文關鍵詞： Prediction, Concrete Strength, Symbiotic Organism Search-Quantum Neural Network
相關次數： 點閱：246  下載：2 
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    • 本文提出了一種新的混合動力技術，稱為共生有機體搜索 - 量子神經網絡（SOS - 量子神經網絡）來預測高性能混凝土的抗壓強度。量子神經網絡是指類神經網絡模型，人工或生物，這依賴於靈感以某種方式從量子力學原理。當SOS是一個新的優化技術。 SOS - 量子神經網絡的性能與人工神經網絡（ANN）和量子神經網絡（QNN）本身相比。 SOS功能是優化內量子神經網絡的所有參數，因此它會提高量子神經網絡在解決HPC案件的能力。結果表明，SOS-QNN高效，準確地解決各種複雜的問題。此外SOS - 量子神經網絡具有基於均方根誤差，MAE，MAPE和R與其他算法比較最佳的性能。

    This paper presents a new hybrid technique called Symbiotic Organism Search-Quantum Neural Network (SOS-QNN) to predict the compressive strength of high performance Concrete. QNN refers to the class of neural network models, artificial or biological, which rely on principles inspired in some way from quantum mechanics. While SOS is a new optimization technique. The performance of SOS-QNN is compared with artificial neural network (ANN) and quantum neural network (QNN) itself. SOS function is to optimize all parameters within QNN so it will improve QNN ability in solving HPC cases. Results indicate that SOS-QNN efficiently and accurately solves various complex problems. Furthermore SOS-QNN has the best performance based on RMSE, MAE, MAPE, and R compare with other algorithms.

    ABSTRACT i ACKNOWLEDGEMENT iii TABLE OF CONTENTS v LIST OF FIGURES ix LIST OF TABLES xi ABBREVIATIONS xiii CHAPTER 1: INTRODUCTION 1 1.1 Research Motivation 1 1.1.1 Significance of Predicting High Performance Concrete Compressive Strength 1 1.1.2 Shortcomings of Predicting High Performance Concrete Compressive Strength 1 1.1.3 Direction to Deal with the Shortcomings 2 1.1.4 Promise of combining QNN and SOS 3 1.2 Research Objective 4 1.3 Research Scope 4 1.4 Research Methodology 4 1.5 Research Outline 7 CHAPTER 2: LITERATURE REVIEW 9 2.1 High Performance Concrete (HPC) 9 2.2 Symbiotic Organism Search 12 2.2.1 Symbiosis in real-world 12 2.2.2 The Symbiotic Organisms Search (SOS) 13 2.2.3 Mutualism Phase 15 2.2.4 Commensalism Phase 15 2.2.5 Parasitism Phase 16 2.3 Artificial Neural Network 20 2.3.1 Activation Functions 21 2.3.2 Learning Method 22 2.3.3 Weighting Factors 24 2.3.4 Threshold 24 2.3.5 Liner Function 24 2.3.6 Threshold Function 25 2.3.7 Piecewise Liner Function 25 2.3.8 Sigmoidal (S shaped) function 26 2.3.9 Type of Artificial Neural Network 27 2.4 Quantum Neural Network Model 30 2.4.1 Quantum Theory 30 2.4.2 Quantum Neuron Model 33 2.4.3 Quantum Complex-valued Neuron Model 38 2.4.4 Node Operation 38 2.4.5 Quantum Neural Network Model 39 2.4.6 Quantum Neural Learning Algorithm 40 2.4.7 Feature of Quantum Neural Network 41 2.4.8 Mapping a Number into Quantum State 43 2.4.9 Quantum State Output Interpretation 43 2.4.10 Compression the classical and quantum manifestations: 44 CHAPTER 3: SYMBIOTIC ORGANISMS SEARCH-QUANTUM NEURAL NETWORK (SOS-QNN) 45 3.1 SOS-QNN Architecture 45 3.2 Numerical Example 49 3.1 Modified Predicted Output Value 50 CHAPTER 4: CASE STUDY 53 4.1 Dataset 53 4.2 Tuning Parameter 53 4.3 Performance Measurement 55 4.4 Result and Discussion 57 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 69 5.1 Conclusions 69 5.2 Future Research and Recommendations 69 REFERENCES 71 APPENDIX A: Historical Data 76 APPENDIX B: MatLab Source Code 102

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