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
Research Engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control, among others. Professors and Graduate students working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control
1.3. Problem statement
1.4. Book Structure
2. Classification of electromechanical systems using Support Vector Machines trained with biologically inspired algorithms
2.2. Classification of electromechanical signals of finger positions treated as large patterns
2.3. The classifier employed is a Support Vector Machine which has been trained using a low computational complexity algorithm such as Kernel Adatron combined with biologically inspired algorithms such as Artificial Bee Colony (ABC), micro-Artificial Bee Colony (_ABC), Differential Evolution (DE) and Particle Swarm Optimization (PSO).
3. Germinal Center inspired algorithm for approximation of free forms using 3d point clouds
3.2. Presentation of a novel optimization algorithm inspired on the functionality of a structure of the human immune system known as Germinal Center
3.3. Application of the Germinal Center algorithm to approximate free 3d forms using point clouds obtained from a 3D laser scanner.
4. Soft Computing Applications in Mobile Robotics
4.2. Obstacle detection
4.3. Nonholonomic robot navigation
4.4. Holonomic robot navigation
4.5. Drone navigation
5. Soft Computing Applications in Robot Vision
5.2. Registration of 3D point clouds
5.3. Robot pose estimation based on Visual Data
5.4. Image tracking
5.5. Visual servoing
6. Particle swarm optimization to improve neural identifiers for discrete-time unknown nonlinear systems
6.2. Particle-swarm-based approach of a Real-time Discrete Neural Identifier for Linear Induction Motors
6.3. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids
7. Bio-inspired algorithms to improve neural controllers for discrete-time unknown nonlinear systems
7.2. Neural-PSO Second Order Sliding Mode Controller for Unknown Discrete-time Nonlinear Systems
7.3. Neural-BFO Second Order Sliding Mode Controller for Unknown Discrete-time Nonlinear Systems
8. Final remarks
- No. of pages:
- © Butterworth-Heinemann 2018
- 1st February 2018
- Paperback ISBN:
Nancy Arana-Daniel received the M.S. and Ph.D. degrees in Computer Science, both from the Center of Research and Advanced Studies, CINVESTAV Unidad Guadalajara, Jalisco, México. She is currently a Research Fellow with the Department of Computer Science, University of Guadalajara, Guadalajara, Jalisco, where she is working, in the Intelligent Systems Research Group. She is director of the research center on artificial intelligence at CUCEI, Universidad de Guadalajara. Her current research interests include applications of geometric algebra, machine learning, optimization, computer vision, pattern recognition, and visually guided robotics.
University of Guadalajara, Guadalajara, Jalisco, Mexico
Carlos Lopez-Franco gained his Ph.D. in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV Unidad Guadalajara, Jalisco, México. At the present he is a full professor at the University of Guadalajara, México, Department of Computer Science. He is currently working with the Intelligent Systems group and he is the head of the department of Computer Sciences at CUCEI, Universidad de Guadalajara. His current research interests include geometric algebra, computer vision, robotics and pattern recognition.
University of Guadalajara, Guadalajara, Jalisco, Mexico
Alma Y. Alanis received her B. Sc degree from Instituto Tecnologico de Durango (ITD, Durango Campus, Durango, Durango) in 2002, and 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 Campus, Mexico) in 2004 and 2007 respectively. Since 2008 she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-2). She has published papers in recognized International Journals and Conferences, along with two international books. She is a Senior Member of the IEEE and Subject and Associated Editor of the Journal of Franklin Institute (Elsevier) and Intelligent Automation & Soft Computing (Taylor & Francis); moreover, she is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record (RCEA-CONACYT), evaluating a wide range of national research projects. She has belonged to important project evaluation committees for national and international research projects. Her research interests are in neural control, backstepping control, block control, and their applications to electrical machines, power systems and robotics.
University of Guadalajara Guadalajara, Jalisco, Mexico