‘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.
Table des matières
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.
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