Bio-inspired algorithms for engineering

Bio-inspired algorithms for engineering

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, com...

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Detalles Bibliográficos
Otros Autores: Alanis, Alma Y., author (author), Arana-Daniel, Nancy, author, Lopez-Franco, Carlos, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Kidlington, England ; Cambridge, Massachusetts : Butterworth-Heinemann 2018.
Edición:First edition
Materias:
Computer algorithms.
Natural computation > Industrial applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630692106719
Tabla de Contenidos:
  • Front Cover
  • Bio-inspired Algorithms for Engineering
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • 1 Bio-inspired Algorithms
  • 1.1 Introduction
  • 1.1.1 Bio-inspired and evolutionary algorithms
  • 1.2 Particle Swarm Optimization
  • 1.3 Arti cial Bee Colony Algorithm
  • 1.4 Micro Arti cial Bee Colony Algorithm
  • 1.5 Differential Evolution
  • 1.6 Bacterial Foraging Optimization Algorithm
  • References
  • 2 Data Classi cation Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron
  • 2.1 Introduction
  • 2.2 Support Vector Machines
  • 2.3 Evolutionary algorithms
  • 2.4 The Kernel Adatron algorithm
  • 2.5 Kernel Adatron trained with evolutionary algorithms
  • 2.6 Results using benchmark repository datasets
  • 2.7 Application to classify electromyographic signals
  • 2.8 Conclusions
  • References
  • 3 Reconstruction of 3D Surfaces Using RBF Adjusted with PSO
  • 3.1 Introduction
  • 3.2 Radial basis functions
  • 3.3 Interpolation of surfaces with RBF and PSO
  • 3.3.1 Ellipsoid of covariance
  • 3.3.2 RBF-PSO and ellipsoid of covariance to interpolate 3D point-clouds
  • 3.3.3 Experimental results
  • 3.4 Conclusion
  • References
  • 4 Soft Computing Applications in Robot Vision
  • 4.1 Introduction
  • 4.2 Image tracking
  • 4.2.1 Normalized cross correlation
  • 4.2.2 Continuous plane to image plane conversion
  • 4.2.3 Algorithm implementation
  • 4.2.4 Experiments
  • 4.3 Plane detection
  • 4.3.1 Description of the method
  • 4.3.2 Simulations results
  • 4.3.2.1 Outliers test
  • 4.3.3 Noise test
  • 4.3.4 Experiments
  • 4.4 Conclusion
  • References
  • 5 Soft Computing Applications in Mobile Robotics
  • 5.1 Introduction to mobile robotics
  • 5.2 Nonholonomic mobile robot navigation
  • 5.2.1 2D projective geometry
  • 5.2.1.1 Representation of points
  • 5.2.1.2 Representation of lines
  • 5.2.1.3 Point line distance.
  • 5.2.2 Robot navigation
  • 5.2.2.1 Nonholonomic robot kinematics
  • 5.2.2.2 Feedback control
  • 5.2.3 Obstacle avoidance using PSO
  • 5.2.3.1 Collision test
  • 5.2.3.2 Visibility test
  • 5.2.3.3 Algorithm
  • 5.3 Holonomic mobile robot navigation
  • 5.3.1 Kinematics of the holonomic robot
  • 5.3.1.1 Inverse kinematics
  • 5.3.1.2 Obstacle avoidance
  • 5.4 Conclusion
  • References
  • 6 Particle Swarm Optimization to Improve Neural Identi ers for Discrete-time Unknown Nonlinear Systems
  • 6.1 Introduction
  • 6.2 Particle-swarm-based approach of a real-time discrete neural identi er for Linear Induction Motors
  • 6.2.1 Preliminaries
  • 6.2.1.1 Discrete-time Recurrent High Order Neural Networks
  • 6.2.1.2 The EKF training algorithm
  • 6.2.1.3 PSO improvement for EKF training algorithm
  • 6.2.2 Neural identi cation
  • 6.2.3 Linear Induction Motor application
  • 6.2.3.1 Motor model
  • 6.2.3.2 Neural identi er design
  • Reduced order nonlinear observer
  • 6.2.3.3 Experimental results
  • 6.2.3.4 Comparison of the EKF-PSO algorithm for neural identi cation
  • 6.3 Neural model with particle swarm optimization Kalman learning for forecasting in smart grids
  • 6.3.1 Neural identi cation
  • 6.3.1.1 EKF training algorithm improved with PSO
  • 6.3.1.2 Regressor structure
  • 6.3.2 Results for wind speed forecasting
  • 6.3.2.1 Comparison of the PSO algorithm for wind speed forecasting
  • 6.3.3 Results for electricity price forecasting
  • 6.4 Conclusions
  • References
  • 7 Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System
  • 7.1 Neural Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
  • 7.1.1 Second-Order Sliding Mode Controller
  • 7.1.2 Neural Second-Order Sliding Mode Controller
  • 7.1.3 Simulation results of the Neural Second-Order Sliding Mode Controller.
  • 7.2 Neural-PSO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
  • 7.2.1 Neural-PSO Second-Order Sliding Mode Controller design
  • 7.2.2 Simulation results of the Neural-PSO Second-Order Sliding Mode Controller
  • 7.3 Neural-BFO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
  • 7.3.1 Neural-BFO Second-Order Sliding Mode Controller design
  • 7.3.2 Simulation results of the Neural-BFO Second-Order Sliding Mode Controller
  • 7.4 Comparative analysis
  • 7.5 Conclusions
  • References
  • 8 Final Remarks
  • Index
  • Back Cover.

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