Applications of Neural Networks in High Assurance Systems [electronic resource] / edited by Johann Schumann, Yan Liu.

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.

"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.