Brain and Nature-Inspired Learning, Computation and Recognition

Released on by Licheng Jiao
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  • Brain and Nature-Inspired Learning, Computation and Recognition Book Detail

  • Author : Licheng Jiao
  • Release Date : 2020-01-18
  • Publisher : Elsevier
  • Genre : Technology & Engineering
  • Pages : 790
  • ISBN 13 : 0128204044
  • File Size : 41,41 MB
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Brain and Nature-Inspired Learning, Computation and Recognition by Licheng Jiao PDF Summary

Book 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. - 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

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