000 | 03630nam a22005775i 4500 | ||
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001 | 978-3-540-79872-9 | ||
003 | DE-He213 | ||
005 | 20161121230550.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2008 gw | s |||| 0|eng d | ||
020 |
_a9783540798729 _9978-3-540-79872-9 |
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024 | 7 |
_a10.1007/978-3-540-79872-9 _2doi |
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050 | 4 | _aTJ210.2-211.495 | |
050 | 4 | _aTJ163.12 | |
072 | 7 |
_aTJFM _2bicssc |
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072 | 7 |
_aTJFD _2bicssc |
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072 | 7 |
_aTEC004000 _2bisacsh |
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072 | 7 |
_aTEC037000 _2bisacsh |
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082 | 0 | 4 |
_a629.8 _223 |
100 | 1 |
_aPatan, Krzysztof. _eauthor. |
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245 | 1 | 0 |
_aArtificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes _h[electronic resource] / _cby Krzysztof Patan. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008. |
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300 |
_aXXII, 206 p. 93 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Control and Information Sciences, _x0170-8643 ; _v377 |
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505 | 0 | _aModelling Issue in Fault Diagnosis -- Locally Recurrent Neural Networks -- Approximation Abilities of Locally Recurrent Networks -- Stability and Stabilization of Locally Recurrent Networks -- Optimum Experimental Design for Locally Recurrent Networks -- Decision Making in Fault Detection -- Industrial Applications -- Concluding Remarks and Further Research Directions. | |
520 | _aAn unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aSystem theory. | |
650 | 0 | _aStatistical physics. | |
650 | 0 | _aDynamical systems. | |
650 | 0 | _aControl engineering. | |
650 | 0 | _aRobotics. | |
650 | 0 | _aMechatronics. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aControl, Robotics, Mechatronics. |
650 | 2 | 4 | _aSystems Theory, Control. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aStatistical Physics, Dynamical Systems and Complexity. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540798712 |
830 | 0 |
_aLecture Notes in Control and Information Sciences, _x0170-8643 ; _v377 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-79872-9 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c500644 _d500644 |