Brain and Nature-Inspired Learning, Computation and Recognition

Häftad, Engelska, 2020

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Beskrivning

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.

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

Produktinformation

Utgivningsdatum:2020-01-21
Mått:191 x 235 x undefined mm
Vikt:1 860 g
Format:Häftad
Språk:Engelska
Antal sidor:788
Förlag:Elsevier Science
ISBN:9780128197950

Utforska kategorier

Artificiell intelligens inom Data och IT

Mer om författaren

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. Ronghua Shang is Professor of the School of Artificial Intelligence at Xidian University. She has authored or co-authored 5 monographs and 80 papers. 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. 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.

Innehållsförteckning

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