Brain and Nature-Inspired Learning, Computation and Recognition. Este artículo no está disponible.Brain and Nature-Inspired Learning, Computation and Recognition

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.

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.