000 | 03435nam a22005055i 4500 | ||
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001 | u373836 | ||
003 | SIRSI | ||
005 | 20160812084205.0 | ||
007 | cr nn 008mamaa | ||
008 | 100306s2010 gw | s |||| 0|eng d | ||
020 |
_a9783642106903 _9978-3-642-10690-3 |
||
040 | _cMX-MeUAM | ||
050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aSchumann, Johann. _eeditor. |
|
245 | 1 | 0 |
_aApplications of Neural Networks in High Assurance Systems _h[recurso electrónico] / _cedited by Johann Schumann, Yan Liu. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010. |
|
300 |
_a280p. 99 illus. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v268 |
|
505 | 0 | _aApplication of Neural Networks in High Assurance Systems: A Survey -- Robust Adaptive Control Revisited: Semi-global Boundedness and Margins -- Network Complexity Analysis of Multilayer Feedforward Artificial Neural Networks -- Design and Flight Test of an Intelligent Flight Control System -- Stability, Convergence, and Verification and Validation Challenges of Neural Net Adaptive Flight Control -- Dynamic Allocation in Neural Networks for Adaptive Controllers -- Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Automotive Engines -- Pitch-Depth Control of Submarine Operating in Shallow Water via Neuro-adaptive Approach -- Stick-Slip Friction Compensation Using a General Purpose Neuro-Adaptive Controller with Guaranteed Stability -- Modeling of Crude Oil Blending via Discrete-Time Neural Networks -- Adaptive Self-Tuning Wavelet Neural Network Controller for a Proton Exchange Membrane Fuel Cell -- Erratum to: Network Complexity Analysis of Multilayer Feedforward Artificial Neural Networks. | |
520 | _a"Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors, and how to pass the tests. A new adaptive structure of V&V is developed in this book, different from the simple six sigma methods usually used for large-scale systems and different from the theorem-based approach used for simplified component subsystems. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aEngineering mathematics. | |
650 | 0 | _aIndustrial engineering. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aAutomotive Engineering. |
650 | 2 | 4 | _aIndustrial and Production Engineering. |
700 | 1 |
_aLiu, Yan. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642106897 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v268 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-10690-3 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
999 |
_c201716 _d201716 |