000			01705nam a2200289 4500
003			EG-NbEJU
005			20241128223842.0
008			241118s2021 nyu gr 000 0 eng d
020			_a9789811316869
020			_a9789811316876
022			_a16197127
040			_aEG-NbEJU _beng _cEG-NbEJU
041			_aeng
050	0	0	_aQ342 _b.R47 2021
245	0	0	_aReservoir Computing : _bTheory , Physical Implementations , and Applications / _cEditors Kohei Nakajima , Ingo Fischer
260			_aNew York : _bSpringer , _c2021
300			_a477 Pages ; _c30 cm
490	0		_aNatural Computing Series
520			_aThis book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications
650			_aArtificial intelligence
650		0	_aComputational complexity
700	1		_aFischer , Ingo _eEditors
700	1		_aNakajima , Kohei _eEditors
901			_aKholoud
902			_aNew_EJUST_1111_ (26)
942			_2lcc _cBK _n0
999			_c7018 _d7018