By Brian J. Taylor
Artificial neural networks are a kind of man-made intelligence that experience the aptitude of studying, turning out to be, and adapting with dynamic environments. having the ability to study and adapt, man made neural networks introduce new capability strategies and techniques to a few of the more difficult difficulties that the USA faces because it pursues the imaginative and prescient of area exploration and develops different method functions that needs to switch and adapt after deployment.
Neural networks are participants of a category of software program that experience the capability to allow clever computational structures able to simulating features of organic pondering and studying. at the moment no criteria exist to make sure and validate neural network-based platforms. NASA autonomous Verification and Validation Facility has gotten smaller the Institute for clinical learn, Inc. to accomplish examine in this subject and advance a complete advisor to appearing V&V on adaptive structures, with emphasis on neural networks utilized in safety-critical or mission-critical applications.
Methods and strategies for the Verification and Validation of synthetic Neural Networks is the end result of the 1st steps in that learn. This quantity introduces a number of the extra promising equipment and strategies used for the verification and validation (V&V) of neural networks and adaptive structures. A accomplished advisor to appearing V&V on neural community platforms, aligned with the IEEE typical for software program Verification and Validation, will keep on with this publication. The NASA IV&V and the Institute for clinical learn, Inc. are operating to be on the leading edge of software program protection and coverage for neural community and adaptive structures.
Methods and systems for the Verification and Validation of synthetic Neural Networks is dependent for study scientists and V&V practitioners in to guarantee neural community software program platforms for destiny NASA missions and different functions. This publication is usually appropriate for graduate-level scholars in desktop technology and desktop engineering.
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Extra resources for Methods and Procedures for the Verification and Validation of Artificial Neural Networks
A description of configuration management as used on the training process should also be present. Information here can include the training data configuration items that were use, the procedures employed for applying the training data configuration items, and identification and tracking of evaluation metrics used throughout the process. If the system makes use of operational monitors (see Chapter 10), their design needs to be a part of the documentation. The project can decide to include these within the neural network documents, to make the operational monitor design a separate document, or to include it within the overall software design document.
NASA Dryden Flight Research Center and NASA Ames Research Center. March 31. PuUum, Laura L. 2003. Draft Guidance for the Independent Verification and Validation of Neural Networks. Institute for Scientific Research, Inc. IVVNN-GUIDE-DOOlUNCLASS-101603. October. M. 1999. A Software Development Process Model for Artificial Neural Networks in Critical Applications. In Proceedings of the 1999 International Conference on Neural Networks (IJCNN'99). C. Rodvold, DM. 2001. Validation and Regulation of Medical Neural Networks.
Variations of ANN Paradigms, involves modifications to the type of neural network used. The outer loop. Selection and Combination of ANN Input Neurons, concerns altering neural network inputs. All design and training from this step is documented in the Network Training Summary. Step 4: Network deployment is completed through commercial tools, automatic code generation provided by commercial tools, or by saving raw neural network data to file with code to load and execute the neural network. The deployment of the neural network is documented in the Network Integration Document.
Methods and Procedures for the Verification and Validation of Artificial Neural Networks by Brian J. Taylor