Guidance for the Verification and Validation of Neural Networks
This book provides guidance on the verification and validation of neural networks/adaptive systems. Considering every process, activity, and task in the lifecycle, it supplies methods and techniques that will help the developer or V&V practitioner be confident that they are supplying an adaptive/neural network system that will perform as intended. Additionally, it is structured to be used as a cross–reference to the IEEE 1012 standard.
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Verification and Validation of Neural Networks Guidance
Recent Changes to IEEE Std 1012
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Acceptance V&V Test adaptive component adaptive neural network adaptive system aircraft algorithms Artificial Neural Networks behavior change assessment computational Concept Documentation configuration files criteria cross-validation domain ensure environment error error functions evaluation fixed neural network flight control function guidance Hazard Analysis HAZOP high-level goals identify IEEE Std IFCS GEN2 impact Implementation initial Installation Package integration testing Lyapunov function modules Multilayer Perceptron NASA neural network architecture neural network design neural network development neural network knowledge neural network software neural network system OLNN online adaptive neural operational monitor parameters performance practitioner preprocessing problem Required Inputs Risk analysis rule extraction Section self-organizing maps Simulink software requirements source code specific Study Example Supplier Development Plans system requirements System V&V Test target techniques test data traditional software training process training set V&V Activity V&V task V&V Test Design V&V Test Execution V&V Test Plan V&V Test Procedure Verification and Validation