Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control.
- Presents real-time implementation and simulation results for all the proposed schemes
- Offers a comparative analysis and rigorous analysis of the convergence of proposed algorithms
- Provides a guide for implementing each application at the end of each chapter
- Includes illustrations, tables and figures that facilitate the reader’s comprehension of the proposed schemes and applications
| Preface |
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xi | |
| Acknowledgments |
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xv | |
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1 Bio-inspired Algorithms |
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1 | (14) |
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1 | (3) |
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1.1.1 Bio-inspired and evolutionary algorithms |
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3 | (1) |
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1.2 Particle Swarm Optimization |
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4 | (1) |
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1.3 Artificial Bee Colony Algorithm |
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5 | (1) |
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1.4 Micro Artificial Bee Colony Algorithm |
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6 | (2) |
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1.5 Differential Evolution |
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8 | (1) |
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1.6 Bacterial Foraging Optimization Algorithm |
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9 | (6) |
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13 | (2) |
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2 Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron |
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15 | (18) |
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15 | (1) |
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2.2 Support Vector Machines |
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16 | (2) |
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2.3 Evolutionary algorithms |
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18 | (1) |
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2.4 The Kernel Adatron algorithm |
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19 | (1) |
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2.5 Kernel Adatron trained with evolutionary algorithms |
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20 | (3) |
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2.6 Results using benchmark repository datasets |
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23 | (3) |
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2.7 Application to classify electromyographic signals |
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26 | (3) |
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29 | (4) |
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30 | (3) |
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3 Reconstruction of 3D Surfaces Using RBF Adjusted with PSO |
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33 | (16) |
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33 | (1) |
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3.2 Radial basis functions |
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34 | (3) |
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3.3 Interpolation of surfaces with RBF and PSO |
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37 | (10) |
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3.3.1 Ellipsoid of covariance |
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37 | (2) |
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3.3.2 RBF-PSO and ellipsoid of covariance to interpolate 3D point-clouds |
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39 | (3) |
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3.3.3 Experimental results |
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42 | (5) |
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47 | (2) |
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48 | (1) |
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4 Soft Computing Applications in Robot Vision |
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49 | (10) |
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49 | (1) |
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50 | (3) |
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4.2.1 Normalized cross correlation |
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50 | (1) |
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4.2.2 Continuous plane to image plane conversion |
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51 | (1) |
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4.2.3 Algorithm implementation |
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51 | (1) |
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52 | (1) |
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53 | (4) |
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4.3.1 Description of the method |
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54 | (1) |
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4.3.2 Simulations results |
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54 | (1) |
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55 | (2) |
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57 | (1) |
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57 | (2) |
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58 | (1) |
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5 Soft Computing Applications in Mobile Robotics |
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59 | (12) |
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5.1 Introduction to mobile robotics |
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59 | (1) |
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5.2 Nonholonomic mobile robot navigation |
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60 | (6) |
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5.2.1 2D projective geometry |
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60 | (1) |
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61 | (2) |
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5.2.3 Obstacle avoidance using PSO |
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63 | (3) |
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5.3 Holonomic mobile robot navigation |
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66 | (2) |
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5.3.1 Kinematics of the holonomic robot |
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67 | (1) |
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68 | (3) |
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69 | (2) |
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6 Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems |
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71 | (36) |
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71 | (1) |
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6.2 Particle-swarm-based approach of a real-time discrete neural identifier for Linear Induction Motors |
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72 | (17) |
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73 | (6) |
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6.2.2 Neural identification |
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79 | (1) |
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6.2.3 Linear Induction Motor application |
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80 | (9) |
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6.3 Neural model with particle swarm optimization Kalman learning for forecasting in smart grids |
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89 | (12) |
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6.3.1 Neural identification |
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92 | (3) |
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6.3.2 Results for wind speed forecasting |
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95 | (4) |
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6.3.3 Results for electricity price forecasting |
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99 | (2) |
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101 | (6) |
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103 | (4) |
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7 Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System |
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107 | (22) |
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7.1 Neural Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems |
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107 | (8) |
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7.1.1 Second-Order Sliding Mode Controller |
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110 | (2) |
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7.1.2 Neural Second-Order Sliding Mode Controller |
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112 | (2) |
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7.1.3 Simulation results of the Neural Second-Order Sliding Mode Controller |
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114 | (1) |
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7.2 Neural-PSO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems |
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115 | (4) |
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7.2.1 Neural-PSO Second-Order Sliding Mode Controller design |
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118 | (1) |
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7.2.2 Simulation results of the Neural-PSO Second-Order Sliding Mode Controller |
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118 | (1) |
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7.3 Neural-BFO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems |
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119 | (5) |
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7.3.1 Neural-BFO Second-Order Sliding Mode Controller design |
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120 | (2) |
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7.3.2 Simulation results of the Neural-BFO Second-Order Sliding Mode Controller |
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122 | (2) |
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124 | (2) |
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126 | (3) |
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126 | (3) |
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129 | (4) |
| Index |
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133 | |
Nancy Arana-Daniel received her B. Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer sicence from Research Center of the National Polytechnic Institute and Advanced Studies, CINVESTAV, in 2003 and 2007 respectively. She is currently a research fellow at the University of Guadalajara, in the Department of Computer Science Mxico, where she is working at the Laboratory of Intelligent Systems and the Research Center for Control Systems and Artificial Intelligence. She is IEEE Senior member and a member of National System of Researchers (SNI-1). She has published several papers in International Journals and Conferences and she has been technical manager of several projects that have been granted by the Nacional Council of Science and Technology (CONACYT). Also, se has collaborated in an international project granted by OPTREAT), She is Associated Editor of the Journal of Franklin Institute (Elsevier). Her research interests focus on applications of geometric algebra, geometric computing, machine learning, bio-inspired optimization, pattern recognition and robot navigation. Carlos Lpez-Franco received the Ph.D. degree in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV, Mexico. He is currently a professor at the University of Guadalajara, Mexico, Computer Science Department, and member of the Intelligent Systems group. He is IEEE Senior member and a member of National System of Researchers) or SNI, level 1. His research interests include geometric algebra, computer vision, robotics and intelligent systems. Dr. Alma Y. Alanis received her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara, Mexico. Since 2008 she has been with University of Guadalajara, where she is currently a Dean of the Technologies for Cyber-Human Interaction Division, CUCEI. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides eight international books. Dr. Alanis is a Senior Member of the IEEE and Subject Editor of the Journal of Franklin Institute, Section Editor at Open Franklin, Technical Editor at ASME/IEEE Transactions on Mechatronics, and Associate Editor at IEEE Transactions on Cybernetics, Intelligent Automation & Soft Computing and Engeenering Applications of Artifical Intelligence. Moreover, Dr. Alanis is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 Dr. Alanis received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Her research interest centers on artificial neural networks, learning systems, intelligent control, and intelligent systems.
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