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100306s2010 gw | s |||| 0|eng d |
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|a 9783642106903
|9 978-3-642-10690-3
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|a 10.1007/978-3-642-10690-3
|2 doi
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|a TA329-348
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|a MAT003000
|2 bisacsh
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|a 519
|2 23
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| 100 |
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|a Schumann, Johann.
|e editor.
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|a Applications of Neural Networks in High Assurance Systems
|c edited by Johann Schumann, Yan Liu.
|h [electronic resource] /
|
| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2010.
|
| 300 |
# |
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|a 280p. 99 illus.
|b online resource.
|
| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Studies in Computational Intelligence,
|v 268
|x 1860-949X ;
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| 505 |
0 |
# |
|a Application 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 -- 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.
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| 520 |
# |
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|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 |
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0 |
|a Engineering.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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|a Engineering mathematics.
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| 650 |
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|a Industrial engineering.
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| 650 |
1 |
4 |
|a Engineering.
|
| 650 |
2 |
4 |
|a Appl.Mathematics/Computational Methods of Engineering.
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| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
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| 650 |
2 |
4 |
|a Automotive Engineering.
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| 650 |
2 |
4 |
|a Industrial and Production Engineering.
|
| 700 |
1 |
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|a Liu, Yan.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783642106897
|
| 830 |
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|a Studies in Computational Intelligence,
|v 268
|x 1860-949X ;
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-10690-3
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| 912 |
# |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
|