Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications.
Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.

Background of the Verification and Validation of Neural Networks.- Augmentation of Current Verification and Validation Practices.- Risk and Hazard Analysis for Neural Network Systems.- Validation of Neural Networks Via Taxonomic Evaluation.- Stability Properties of Neural Networks.- Neural Network Verification.- Neural Network Visualization Techniques.- Rule Extraction as a Formal Method.- Automated Test Generation for Testing Neural Network Systems.- Run-Time Assessment of Neural Network Control Systems.