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EBOOK
Title Applications of neural networks in high assurance systems / Johann Schumann and Yan Liu (eds.).
Imprint Berlin : Springer, 2010.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Studies in computational intelligence, 1860-949X ; v. 268
Studies in computational intelligence ; v. 268.
Subject Neural networks (Computer science)
System safety.
Alt Name Schumann, Johann M., 1960-
Liu, Yan, 1972-
Description 1 online resource (xv, 245 pages) : illustrations (some color).
polychrome rdacc
Bibliography Note Includes bibliographical references.
Contents 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 -- 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.
Summary "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.
Access License restrictions may limit access.
Note English.
Print version record.
ISBN 9783642106903
3642106900
1280003294
9781280003295
9783642106897
3642106897
ISBN/ISSN 10.1007/978-3-642-10690-3
OCLC # 630115453
Additional Format Print version: Applications of neural networks in high assurance systems. Berlin : Springer, 2010 9783642106897 3642106897 (OCoLC)471802816


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