This is the first book to directly address a key part of neural network technology: state-of-the-art methods used to pass the tough verification and validation standards required in many safety-critical applications.
"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.
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.
