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Elsevier

Brain and Nature-Inspired Learning, Computation and Recognition

  • 1st Edition - January 18, 2020
  • Latest edition
  • Authors: Licheng Jiao, Ronghua Shang, Fang Liu, Weitong Zhang
  • Language: English

Brain and Nature-Inspired Learning, Computation and Recognition presents a systematic analysis of neural networks, natural computing, machine learning and compression, algorithm… Read more

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Description

Brain and Nature-Inspired Learning, Computation and Recognition presents a systematic analysis of neural networks, natural computing, machine learning and compression, algorithms and applications inspired by the brain and biological mechanisms found in nature. Sections cover new developments and main applications, algorithms and simulations. Developments in brain and nature-inspired learning have promoted interest in image processing, clustering problems, change detection, control theory and other disciplines. The book discusses the main problems and applications pertaining to bio-inspired computation and recognition, introducing algorithm implementation, model simulation, and practical application of parameter setting.

Readers will find solutions to problems in computation and recognition, particularly neural networks, natural computing, machine learning and compressed sensing. This volume offers a comprehensive and well-structured introduction to brain and nature-inspired learning, computation, and recognition.

Key features

  • Presents an invaluable systematic introduction to brain and nature-inspired learning, computation and recognition
  • Describes the biological mechanisms, mathematical analyses and scientific principles behind brain and nature-inspired learning, calculation and recognition
  • Systematically analyzes neural networks, natural computing, machine learning and compression, algorithms and applications inspired by the brain and biological mechanisms found in nature
  • Discusses the theory and application of algorithms and neural networks, natural computing, machine learning and compression perception

Readership

Researchers and advanced students in brain and nature-inspired learning, intelligent control, natural computing, machine learning, compressed sensing, signal processing, and image processing; Data scientists and those interested in statistical learning. Researchers and postgraduate students in education

Table of contents

1. Introduction2. The models and structure of neural network3. Theoretical Basis of Natural Computation4. Theoretical basis of machine learning5. Theoretical basis of compressive sensing6. SAR image7. POLSAR Image Classification8. Hyperspectral Image9. Multiobjective Evolutionary Algorithm (MOEA) based Sparse Clustering10. MOEA Based Community Detection11. Evolutionary Computation Based Multiobjective Capacitated Arc Routing Optimizations12. Multiobjective Optimization Algorithm Based Image Segmentation13. Graph regularized Feature Selection based on spectral learning and subspace learning14. Semi-supervised learning based on mixed knowledge information and nuclear norm regularization15. Fast clustering methods based on learning spectral embedding16. Fast clustering methods based on affinity propagation and density-weighted17. SAR image processing based on similarity measure and discriminant feature learning18. Hyperspectral image processing based on sparse learning and sparse graph19. Non-convex compressed sensing framework based on block strategy and overcomplete dictionary20. The sparse representation combined with FCM in compressed sensing21. Compressed sensing by collaborative reconstruction22. Hyperspectral image classification based on spectral information divergence and sparse representation

Product details

  • Edition: 1
  • Latest edition
  • Published: January 21, 2020
  • Language: English

About the authors

LJ

Licheng Jiao

Licheng Jiao is Distinguished Professor of the School of Artificial Intelligence at Xidian University in Xi’an, China. He is IEEE Fellow, IET Fellow. He is also the vice president of CAAI, the chairman of awards and recognition committee, the Councilor of the Chinese Institute of Electronics, and an expert of academic degrees committee of the state council.
Affiliations and expertise
Distinguished Professor, School of Artificial Intelligence, Xidian University, Xi’an, China.

RS

Ronghua Shang

Ronghua Shang is Professor of the School of Artificial Intelligence at Xidian University. She has authored or co-authored 5 monographs and 80 papers.
Affiliations and expertise
Professor, School of Artificial Intelligence, Xidian University, Xi’an, China

FL

Fang Liu

Fang Liu is a Professor of the School of Artificial Intelligence at Xidian University. She has authored or co-authored over 10 monographs and over 80 papers.
Affiliations and expertise
Professor, School of Artificial Intelligence, Xidian University, Xi’an, China

WZ

Weitong Zhang

Weitong Zhang is a PhD researcher in the School of Artificial Intelligence at Xidian University. Her research focuses on dynamic complex networks and she has published several papers in the field.
Affiliations and expertise
PhD researcher, School of Artificial Intelligence, Xidian University, Xi’an, China

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