Applications of Neural Networks in High Assurance Systems

1. Auflage. 2010
ISBN: 978-3-642-10689-7
Verlag: Springer

Buch, Englisch, Band 268, 248 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 613 g

Reihe: Studies in Computational Intelligence

"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.

Zielgruppe

Research

Autoren/Hrsg.

Schumann, Johann M.Ph.

Liu, Yan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik

Inhaltsverzeichnis

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.