| Intelligent Control for LabVIEW | p. 1 |
| Introduction | p. 1 |
| Intelligent Control in Industrial Applications | p. 3 |
| LabVIEW | p. 4 |
| References | p. 7 |
| Fuzzy Logic | p. 9 |
| Introduction | p. 9 |
| Industrial Applications | p. 9 |
| Background | p. 10 |
| Uncertainty in Information | p. 11 |
| Concept of Fuzziness | p. 11 |
| Foundations of Fuzzy Set Theory | p. 11 |
| Fuzzy Sets | p. 12 |
| Boolean Operations and Terms | p. 14 |
| Fuzzy Operations and Terms | p. 15 |
| Properties of Fuzzy Sets | p. 18 |
| Fuzzification | p. 18 |
| Extension Principle | p. 21 |
| Alpha Cuts | p. 23 |
| The Resolution Principle | p. 24 |
| Fuzziness of Uncertainty | p. 24 |
| Possibility and Probability Theories | p. 25 |
| Fuzzy Logic Theory | p. 26 |
| From Classical to Fuzzy Logic | p. 26 |
| Fuzzy Logic and Approximate Reasoning | p. 26 |
| Fuzzy Relations | p. 28 |
| Properties of Relations | p. 28 |
| Max-Min Composition | p. 29 |
| Max-Star Composition | p. 30 |
| Max-Average Composition | p. 31 |
| Fuzzy Linguistic Descriptions | p. 31 |
| The Fuzzy Logic Controller | p. 33 |
| Linguistic Variables | p. 33 |
| Membership Functions | p. 33 |
| Rules Evaluation | p. 33 |
| Mamdani Fuzzy Controller | p. 34 |
| Structure | p. 34 |
| Fuzzification | p. 34 |
| Rules Evaluation | p. 35 |
| Defuzzification | p. 35 |
| Tsukamoto Fuzzy Controller | p. 35 |
| Takagi-Sugeno Fuzzy Controller | p. 36 |
| Structure | p. 36 |
| Fuzzification | p. 36 |
| Rules Evaluation | p. 36 |
| Crisp Outputs | p. 37 |
| Implementation of the Fuzzy Logic Controllers Using the Intelligent Control Toolkit for LabVIEW | p. 37 |
| Fuzzification | p. 38 |
| Rules Evaluation | p. 40 |
| Defuzzification: Crisp Outputs | p. 41 |
| Classical Control Example | p. 43 |
| References | p. 46 |
| Futher Reading | p. 46 |
| Artificial Neural Networks | p. 47 |
| Introduction | p. 47 |
| Artificial Neural Network Classification | p. 55 |
| Artificial Neural Networks | p. 56 |
| Perception | p. 57 |
| Multi-layer Neural Network | p. 60 |
| Trigonometric Neural Networks | p. 71 |
| Kohonen Maps | p. 79 |
| Bayesian or Belief Networks | p. 84 |
| References | p. 87 |
| Futher Reading | p. 88 |
| Neuro-fuzzy Controller Theory and Application | p. 89 |
| Introduction | p. 89 |
| The Neuro-fuzzy Controller | p. 90 |
| Trigonometric Artificial Neural Networks | p. 91 |
| Fuzzy Cluster Means | p. 96 |
| Predictive Method | p. 98 |
| Results Using the Controller | p. 100 |
| Controller Enhancements | p. 101 |
| ANFIS: Adaptive Neuro-fuzzy Inference Systems | p. 106 |
| ANFIS Topology | p. 108 |
| References | p. 122 |
| Futher Reading | p. 122 |
| Genetic Algorithms and Genetic Programming | p. 123 |
| Introduction | p. 123 |
| Evolutionary Computation | p. 123 |
| Industrial Applications | p. 124 |
| Biological Terminology | p. 125 |
| Search Spaces and Fitness | p. 125 |
| Encoding and Decoding | p. 125 |
| Genetic Algorithm Stages | p. 126 |
| Initialization | p. 127 |
| Selection | p. 128 |
| Crossover | p. 129 |
| Mutation | p. 130 |
| Genetic Algorithms and Traditional Search Methods | p. 134 |
| Applications of Genetic Algorithms | p. 135 |
| Pros and Cons of Genetic Algorithms | p. 136 |
| Selecting Genetic Algorithm Methods | p. 136 |
| Messy Genetic Algorithm | p. 137 |
| Optimization of Fuzzy Systems Using Genetic Algorithms | p. 138 |
| Coding Whole Fuzzy Partitions | p. 138 |
| Standard Fitness Functions | p. 139 |
| Coding Rule Bases | p. 139 |
| An Application of the ICTL for the Optimization of a Navigation System for Mobile Robots | p. 140 |
| Genetic Programming Background | p. 143 |
| Genetic Programming Definition | p. 143 |
| Historical Background | p. 144 |
| Industrial Applications | p. 144 |
| Advantages of Evolutionary Algorithms | p. 144 |
| Genetic Programming Algorithm | p. 145 |
| Length | p. 146 |
| Genetic Programming Stages | p. 146 |
| Initialization | p. 146 |
| Fitness | p. 147 |
| Selection | p. 147 |
| Crossover | p. 147 |
| Mutation | p. 148 |
| Variations of Genetic Programming | p. 149 |
| Genetic Programming in Data Modeling | p. 150 |
| Genetic Programming Using the ICTL | p. 150 |
| References | p. 153 |
| Futher Reading | p. 154 |
| Simulated Annealing, FCM, Partition Coefficients and Tabu Search | p. 155 |
| Introduction | p. 155 |
| Introduction to Simulated Annealing | p. 156 |
| Pattern Recognition | p. 157 |
| Introduction to Tabu Search | p. 157 |
| Industrial Applications of Simulated Annealing | p. 158 |
| Industrial Applications of Fuzzy Clustering | p. 158 |
| Industrial Applications of Tabu Search | p. 158 |
| Simulated Annealing | p. 159 |
| Simulated Annealing Algorithm | p. 161 |
| Sample Iteration Example | p. 163 |
| Example of Simulated Annealing Using the Intelligent Control Toolkit for LabVIEW | p. 163 |
| Fuzzy Clustering Means | p. 166 |
| FCM Example | p. 170 |
| Partition Coefficients | p. 172 |
| Reactive Tabu Search | p. 173 |
| Introduction to Reactive Tabu Search | p. 173 |
| Memory | p. 174 |
| References | p. 189 |
| Futher Reading | p. 190 |
| Predictors | p. 191 |
| Introduction to Forecasting | p. 191 |
| Industrial Applications | p. 192 |
| Forecasting Methods | p. 193 |
| Qualitative Methods | p. 193 |
| Quantitative Methods | p. 194 |
| Regression Analysis | p. 194 |
| Exponential Smoothing | p. 194 |
| Simple-exponential Smoothing | p. 195 |
| Simple-exponential Smoothing Algorithm | p. 195 |
| Double-exponential Smoothing | p. 196 |
| Holt-Winter Method | p. 197 |
| Non-seasonal Box-Jenkins Models | p. 198 |
| General Box-Jenkins Model | p. 199 |
| Minimum Variance Estimation and Control | p. 200 |
| Example of Predictors Using the Intelligent Control Toolkit for LabVIEW (ICTL) | p. 202 |
| Exponential Smoothing | p. 202 |
| Box-Jenkins Method | p. 203 |
| Minimum Variance | p. 204 |
| Gray Modeling and Prediction | p. 205 |
| Modeling Procedure of the Gray System | p. 206 |
| Example of a Gray Predictor Using the ICTL | p. 207 |
| References | p. 210 |
| Futher Reading | p. 210 |
| Index | p. 211 |
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