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

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.

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

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