Guidance for the Verification and Validation of Neural Networks
John Wiley & Sons, Mar 9, 2007 - Computers - 133 pages
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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Acceptance Test Acceptance V&V Test adaptive component adaptive neural network adaptive system aircraft algorithms behavior change assessment computational Concept Documentation configuration files criteria Criticality Analysis 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 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 solution source code specific Study Example Supplier Development Plans system requirements System V&V Test target techniques test data Traceability analysis traditional software training process training set V&V Activity V&V task V&V Test Design V&V Test Plan V&V Test Procedure Verification and Validation