000			02097pam a2200289 a 4500
001			96049477
003			APU
005			20160309051504.0
008			961107s1997 nyua b 001 0 eng
010			_a96049477
020			_a0071155546 (pbk.)
020			_a9780071155540 (pbk.)
040			_aDLC _cAPU _dBAHAR _dSM _beng
050	0	0	_aQA76.87 _b.S3 1997
082	0	0	_221 _a006.32 _bSCH 1997
100	1		_aSchalkoff, Robert J. _9776
245	1	0	_aArtificial neural networks / _cRobert J. Schalkoff.
260			_aNew York : _bMcGraw-Hill, _cc1997.
300			_axxi, 422 p. : _bill. ; _c24 cm.
440			_aComputer Science Series. _911790
490	1		_aArtificial intelligence.
504			_aIncludes bibliographical references and index.
520			_aArtificial Neural Networks brings together an identifiable core of ideas, techniques, and applications that characterize this emerging field. The text is intended for beginning graduate/advanced undergraduate students as well as practicing engineers and scientists. The text is suitable for use in a one- or two-semester course and may be supplemented by individual student projects and readings from the literature. Numerous exercises are presented to challenge and motivate the reader to further explore relevant concepts. Many of these exercises can be expanded into projects and thesis work. No previous experience in this field is assumed, although readers familiar with signal processing, linear algebra, pattern recognition, and other related areas will find the book easier to read. The book is meant to be largely self-contained and suitable for students in the disciplines of electrical and computer engineering, computer science, mathematics, physics, and related disciplines. While the primary objective of the text is to provide a teaching tool, practicing engineers and scientists are likely to find the clear, concept-based treatment useful in updating their backgrounds.
650		0	_aNeural networks (Computer science) _9778
830		0	_aMcGraw-Hill series in artificial intelligence. _911791
942			_2lcc _cBook _02
999			_c6388 _d6388