| Title Page | 2 |
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| Preface | 6 |
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| Organization | 8 |
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| Contents | 11 |
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| Fuzzy Information | 27 |
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| Multi-step Offline Handwritten Chinese Characters Segmentation with GA | 27 |
| Introduction | 27 |
| Rough Segmentation Based on GA | 28 |
| HIT-MW Dataset | 28 |
| Project Profile Histogram Method | 28 |
| Coding | 29 |
| Fitness Function | 29 |
| GA Parameters Selection | 30 |
| GA Parameters Selection | 30 |
| Over-Segmentation Characters Mergence | 30 |
| Punctuation and Digital Number Estimation | 30 |
| Centroids Normalization | 31 |
| Merging Result | 31 |
| Re-segment of Insufficient Segmentation Characters | 32 |
| Viterbi Algorithm | 32 |
| Hidden Markov Model of Character Block | 32 |
| Re-segmentation by Viterbi Algorithm | 34 |
| Paths Optimization | 34 |
| Modified New Optimal Rules | 35 |
| Experiments and Analysis | 36 |
| References | 36 |
| A New Covert Image Communication Approach with HVS and GFCM | 38 |
| Introduction | 38 |
| Chaotic System and Sequences Modulation | 39 |
| GFCM of Several Parameters of Image Blocks | 40 |
| Selection of DCT Coefficients and Maximal Strength | 41 |
| Digital Information Embedding and Extracting | 42 |
| Simulation Results and Analysis | 43 |
| Conclusions | 45 |
| References | 45 |
| Sliding Model Fuzzy Control for a Bridge Crane | 47 |
| Introduction | 47 |
| Mathematical Model of Bridge Crane | 48 |
| Experiment Analysis | 50 |
| Simulation and Experiments | 51 |
| Conclusions | 53 |
| References | 53 |
| Apply Multi-class Fuzzy Support Vector Machines to Product-Form-Image Prediction | 55 |
| Introduction | 55 |
| Kansei Image Prediction Modeling | 56 |
| Kansei Information System | 56 |
| Kansei Image Prediction | 56 |
| Multi-Class Fuzzy SVMs | 57 |
| Support Vector Machines | 57 |
| Multi-class SVMs | 59 |
| Fuzzy SVMs | 59 |
| Kansei Image Formalization Based on MF-SVM | 61 |
| Case Study | 62 |
| Kansei Image Information | 62 |
| Classification by MF-SVMs | 63 |
| Training and Predict for New Products | 63 |
| Conclusions and Discussion | 63 |
| References | 64 |
| The Study on Motor Fuzzy Neural Network Controller Based on Fuzzy Soft Handoff | 65 |
| Introduction | 65 |
| Paralleled FNN Controller with Switching Control | 66 |
| Fuzzy Sub-controller | 66 |
| Neural Network PID Sub-controller | 67 |
| Fuzzy Soft Handoff Control Technology | 68 |
| Hard Handoff | 68 |
| Soft Handoff | 68 |
| Fuzzy Soft Handoff [7] | 70 |
| Simulation of DC Motor System | 72 |
| Conclusion | 73 |
| References | 73 |
| Study on Armament System of System Based on Fuzzy Cognitive Map | 75 |
| Introduction | 75 |
| Characteristics of SoS | 75 |
| The Challenge | 76 |
| Fuzzy Cognitive Map | 77 |
| Research on Armament SoS Based on FCM | 78 |
| System Modeling | 79 |
| Simulation Analysis | 81 |
| Conclusion | 82 |
| References | 82 |
| Research on Multiple Objective Decision Model of the Security of On-Line Shopping Based on Fuzzy Information Theory | 84 |
| Introduction | 84 |
| Multilevel Multiple Objective Desision Theory | 85 |
| Build Up the Multiple Objective Decision Set | 85 |
| Build Up the Multilevel Multiple Objective Comment Set | 85 |
| Build Up the Weight Set | 85 |
| Build Up the Multiple Objective Evaluation Membership Matrix | 86 |
| Second-Level Multiple Objective Evaluation | 87 |
| Evaluation of the Security of on-line Shopping | 87 |
| Instance | 87 |
| Build Up Evaluation Set of the Multiple Objective Decision | 87 |
| Gather Information about the Fuzzy Multiple Objective Decision | 88 |
| Calculation of Fuzzy Index’s Weight | 88 |
| Fuzzy Multiple Objective Decision | 88 |
| Conclusion | 90 |
| References | 91 |
| Recognition of Blood Cell Images Based on Color Fuzzy Clustering | 92 |
| Introduction | 92 |
| Methods and Materials | 93 |
| Blood Cell Images | 93 |
| Fuzzy Clustering Algorithm | 93 |
| Color Clustering Analyzing of Blood Cell Image | 94 |
| Recognition by Shape | 96 |
| Implementation by Java Program | 97 |
| Results and Discussion | 97 |
| Conclusions | 98 |
| References | 98 |
| Study on Automatic Detection of Airplane Object in Remote Sensing Images | 99 |
| Introduction | 99 |
| The Research Actuality and Development Current of the Automatic Detection of the Airplane Goal | 100 |
| The Research Actuality of the Airplane Goal Recognition | 100 |
| The Existing Problems and Development Trends in the Automatic Recognition of the Airplane Goals | 100 |
| Basic Theory and Application of the Mathematical Morphology | 101 |
| Summary of Mathematical Morphology | 101 |
| Binary Morphological | 102 |
| The Basic Concepts and Operations of the Gray Mathematical Morphology | 102 |
| Aircraft Target Detection Based on Mathematical Morphology | 103 |
| MATLAB Summarization | 103 |
| The Realization of MATLAB Based on Mathematical Morphology— Aircraft Automatic Detection as an Example | 104 |
| Conclusion | 105 |
| References | 106 |
| Study on the Affection of Gear Fault Diagnosis Bases on HHT by Noises | 108 |
| Introduction | 108 |
| Theory and Method | 109 |
| EMD | 109 |
| Hilbert Transforms (HT) | 111 |
| Hilbert Marginal Spectrum | 111 |
| EEMD | 111 |
| De-Noising Based on Wavelet Packet Decomposition Coefficient Shrinkage | 112 |
| Fault Diagnosis Based on HHT and Neural Network | 113 |
| Case Study | 113 |
| Fault Diagnosis Based on the Pattern Matching of HHT Marginal Spectrum | 114 |
| Fault Diagnosis Based on HHT-Neural Network | 116 |
| Conclusions | 116 |
| References | 117 |