Brain and Nature-Inspired Learning, Computation and Recognition - 1st Edition - ISBN: 9780128197950

Brain and Nature-Inspired Learning, Computation and Recognition

1st Edition

Authors: Licheng Jiao Ronghua Shang Fang Liu Weitong Zhang
Paperback ISBN: 9780128197950
Imprint: Elsevier
Published Date: 31st January 2020
Page Count: 788
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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. Introduction
    2. The models and structure of neural network
    3. Theoretical Basis of Natural Computation
    4. Theoretical basis of machine learning
    5. Theoretical basis of compressive sensing
    6. SAR image
    7. POLSAR Image Classification
    8. Hyperspectral Image
    9. Multiobjective Evolutionary Algorithm (MOEA) based Sparse Clustering
    10. MOEA Based Community Detection
    11. Evolutionary Computation Based Multiobjective Capacitated Arc Routing Optimizations
    12. Multiobjective Optimization Algorithm Based Image Segmentation
    13. Graph regularized Feature Selection based on spectral learning and subspace learning
    14. Semi-supervised learning based on mixed knowledge information and nuclear norm regularization
    15. Fast clustering methods based on learning spectral embedding
    16. Fast clustering methods based on affinity propagation and density-weighted
    17. SAR image processing based on similarity measure and discriminant feature learning
    18. Hyperspectral image processing based on sparse learning and sparse graph
    19. Non-convex compressed sensing framework based on block strategy and overcomplete dictionary
    20. The sparse representation combined with FCM in compressed sensing
    21. Compressed sensing by collaborative reconstruction
    22. Hyperspectral image classification based on spectral information divergence and sparse representation

Details

No. of pages:
788
Language:
English
Copyright:
© Elsevier 2020
Published:
31st January 2020
Imprint:
Elsevier
Paperback ISBN:
9780128197950

About the Author

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.

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

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

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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