000 | 03137nam a22004935i 4500 | ||
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001 | 978-3-540-73762-9 | ||
003 | DE-He213 | ||
005 | 20140220084520.0 | ||
007 | cr nn 008mamaa | ||
008 | 100702s2010 gw | s |||| 0|eng d | ||
020 |
_a9783540737629 _9978-3-540-73762-9 |
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024 | 7 |
_a10.1007/978-3-540-73762-9 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aTJFM1 _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aHe, Xingui. _eauthor. |
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245 | 1 | 0 |
_aProcess Neural Networks _h[electronic resource] : _bTheory and Applications / _cby Xingui He, Shaohua Xu. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010. |
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300 |
_a240p. 78 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aAdvanced Topics in Science and Technology in China, _x1995-6819 |
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505 | 0 | _aArtificial Neural Networks -- Process Neurons -- Feedforward Process Neural Networks -- Learning Algorithms for Process Neural Networks -- Feedback Process Neural Networks -- Multi-aggregation Process Neural Networks -- Design and Construction of Process Neural Networks -- Application of Process Neural Networks. | |
520 | _a"Process Neural Network: Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks and enhances the expression capability for practical problems, with broad applicability to solving problems relating to processes in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are closely examined. The application methods, network construction principles, and optimization algorithms of process neural networks in practical fields, such as nonlinear time-varying system modeling, process signal pattern recognition, dynamic system identification, and process forecast, are discussed in detail. The information processing flow and the mapping relationship between inputs and outputs of process neural networks are richly illustrated. Xingui He is a member of Chinese Academy of Engineering and also a professor at the School of Electronic Engineering and Computer Science, Peking University, China, where Shaohua Xu also serves as a professor. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aOptical pattern recognition. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aPattern Recognition. |
700 | 1 |
_aXu, Shaohua. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540737612 |
830 | 0 |
_aAdvanced Topics in Science and Technology in China, _x1995-6819 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-73762-9 |
912 | _aZDB-2-SCS | ||
999 |
_c111211 _d111211 |