DNA Computing Based Genetic Algorithm -- Applications in Industrial Process Modeling and Control

Description

This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities. Jili Tao received her B.Sc. and Ph.D. from Central South University and Zhejiang University, China, in 2004 and 2007, respectively. She is currently a Professor at the Institute of Ningbo Technology, Ningbo, China. Her research interests include intelligent optimization, modeling and its applications to electronic system design and control system design. Ridong Zhang received his Ph.D. in Control Science and Engineering from Zhejiang University, Hangzhou, China, in 2007. From 2007 to 2015, he was a Full Professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou. Since 2015, he has been a Visiting Professor at the Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology. His research interests include process modeling, model predictive control and nonlinear systems. Yong Zhu, received M.Sc. degrees from HangZhou DianZi University, China, in 2004. He is currently a lecturer in the Institute of Ningbo technology, Ningbo, China. Meanwhile he has been a Ph.D. candidate in Ningbo University. His present research interests in electronic system design and advanced control system design. Contents......Page 5 1.1 Standard Genetic Algorithm......Page 10 1.2.1 Theoretical Research of GA......Page 11 1.2.3 Constraint Handling in GA......Page 13 1.2.4 Multi-objective Genetic Algorithm......Page 15 1.2.5 Applications of GA......Page 17 1.3.1 DNA Molecular Structure of DNA Computing......Page 20 1.3.2 Biological Operators of DNA Computing......Page 21 1.4 The Main Content of This Book......Page 22 References......Page 25 2.1 Introduction......Page 34 2.2.1 Digital Encoding of RNA Sequence......Page 35 2.2.2 Operations of RNA Sequence......Page 36 2.2.3 Encoding and Operators in RNA-GA......Page 37 2.3 Global Convergence Analysis of RNA-GA......Page 43 2.4.1 Test Functions......Page 46 2.4.2 Adaptability of the Parameters......Page 48 2.4.3 Comparisons Between RNA-GA and SGA......Page 50 2.5 Summary......Page 61 References......Page 63 3.1 Introduction......Page 65 3.2 Problem Description and Constraint Handling......Page 66 3.3.1 DNA Double-Helix Encoding......Page 67 3.3.2 DNA Computing Based Operators......Page 69 3.3.3 Hybrid Genetic Algorithm with SQP......Page 72 3.3.4 Convergence Rate Analysis of DNA-DHGA......Page 74 3.4.2 Simulation Analysis......Page 78 3.5 Summary......Page 83 Appendix......Page 84 References......Page 86 4.1 Introduction......Page 88 4.2 Multi-objective Optimization Problems......Page 90 4.3.1 RNA Encoding......Page 91 4.3.2 Pareto Sorting and Density Information......Page 92 4.3.3 Elitist Archiving and Maintaining Scheme......Page 93 4.3.4 DNA Computing Based Crossover and Mutation Operators......Page 96 4.3.6 Convergence Analysis of DNA-MOGA......Page 97 4.4.1 Test Functions and Performance Metrics......Page 99 4.4.2 Calculation Results......Page 100 Appendix......Page 103 References......Page 106 5.1 Introduction......Page 108 5.2 Problem Description of System Identification......Page 110 5.2.1 Lumping Models for a Heavy Oil Thermal Cracking Process......Page 111 5.2.2 Parameter Estimation of FCC Unit Main Fractionator......Page 112 5.3.1 Formulation of Gasoline Blending Scheduling......Page 118 5.3.2 Optimization Results for Gasoline Blending Scheduling......Page 120 Appendix......Page 123 References......Page 124 6.1 Introduction......Page 126 6.2 The Coke Unit......Page 129 6.3 RBF Neural Network......Page 130 6.4.1 Encoding and Decoding......Page 133 6.4.2 Fitness Function......Page 134 6.4.3 Operators of RBFNN Optimization......Page 135 6.4.4 Procedure of the Algorithm......Page 137 6.4.5 Temperature Modeling in a Coke Furnace......Page 138 6.5 Improved MOEA Based RBF Neural Network for Chamber Pressure......Page 142 6.5.1 Encoding of IMOEA......Page 145 6.5.3 Operators of IMOEA for RBFNN......Page 150 6.5.4 The Procedure of IMOEA......Page 152 6.5.5 The Chamber Pressure Modeling in a Coke Furnace......Page 153 6.6.1 RV Criterion in PCA Variable Selection......Page 161 6.6.3 Operators of INSGA-II......Page 163 6.6.5 Main Disturbance Modeling of Chamber Pressure......Page 165 References......Page 171 7.1 Introduction......Page 174 7.2.1 T-S Fuzzy ARX Model......Page 176 7.2.2 T-S Fuzzy Plus Tah Function Model......Page 178 7.3 Improved GA based T-S Fuzzy ARX Model Optimization......Page 179 7.3.1 Hybrid Encoding Method......Page 180 7.3.2 Objectives of T-S Fuzzy Modeling......Page 181 7.3.3 Operators of IGA for T-S Fuzzy Model......Page 182 7.3.5 Computing Complexity Analysis......Page 184 7.3.6 Oxygen Content Modeling by Fuzzy ARX Model......Page 185 7.4.1 Encoding of IGA for Fuzzy Neural Network......Page 189 7.4.2 Operators of IGA for New Fuzzy Model......Page 191 7.4.3 Liquid Level Modeling by Nonlinear Fuzzy Neural Network......Page 192 7.5 Summary......Page 193 References......Page 197 8.1 Introduction......Page 199 8.2 DPS Modeling Issue......Page 200 8.2.1 Time/Space Separation via PCA......Page 201 8.2.2 Decoupled ARX Model Identification......Page 204 8.2.3 RBF Neural Network Modeling......Page 205 8.2.4 Structure and Parameter Optimization by GA......Page 207 8.2.5 Encoding Method......Page 208 8.3 Simulation Results......Page 210 8.3.1 Catalytic Rod......Page 211 8.3.2 Heat Conduction Equation......Page 221 8.4 Summary......Page 222 References......Page 225 9.1 Introduction......Page 227 9.2.1 Process Model Formulation......Page 229 9.2.2 PID Controller Design......Page 231 9.2.3 GA-Based Weighting Matrix Tuning......Page 232 9.2.4 The Chamber Pressure Control by PFC-PID......Page 234 9.3.1 Neuron Controller......Page 242 9.3.2 Simple Fuzzy PI Control......Page 243 9.3.3 Fuzzy Neuron Hybrid Control (FNHC)......Page 245 9.3.4 Parameters Optimization of RNA-GA......Page 246 9.3.5 Continuous Steel Casting Description......Page 247 9.3.6 FNHC Controller Performance Analysis......Page 249 9.4.1 Generalized Hermite-Biehler Theorem......Page 255 9.4.2 Hermite-Biehler Theorem Based PID Controller Stabilizing......Page 256 9.4.3 Optimizing PID Controller Parameters Based on Stabilization Subspaces......Page 259 9.4.4 Simulation for Optimization of PID Controllers......Page 260 9.5 Summary......Page 263 References......Page 264 10.1 Introduction......Page 267 10.3 Q-Learning Based Fuzzy Energy Management Controller......Page 269 10.3.1 Fuzzy Energy Management Controller......Page 270 10.3.2 Q-Learning in HEV Energy Control......Page 273 10.3.4 Initial Value Optimization of Q-Table......Page 275 10.3.5 Procedure of Improved Q-Learning Fuzzy EMS......Page 277 10.3.6 Real-Time Energy Management......Page 278 References......Page 279