Download Guidance for the verification and validation of neural by Taylor, Brian J.; Darrah, Marjorie A.; Pullum, Laura L PDF

By Taylor, Brian J.; Darrah, Marjorie A.; Pullum, Laura L

This publication presents counsel at the verification and validation of neural networks/adaptive platforms. contemplating each strategy, job, and job within the lifecycle, it provides tools and methods that would support the developer or V&V practitioner be convinced that they're offering an adaptive/neural community approach that would practice as meant. also, it truly is based for use as a cross-reference to the IEEE 1012 average

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Rule extraction, rule initialization, and rule insertion can all be used for V&V purposes throughout the development life cycle. During the Concept, Requirements, Design, Implementation, and Testing Activities, different aspects of these techniques can be applied. Kurd, Kelly, and Austin [2003] describe a model that uses rule initialization, extraction, and insertion and ties the hazard analysis into the development of the neural networks' knowledge. 2. Rule extraction offers the possibility of requirements traceability throughout the entire development life cycle.

1 Nested Loop Model of Neural Network Development Process The following information is summarized from "A Software Development Process Model for Artificial Neural Networks in Critical Applications," written by David M. Rodvold [1999]. Rodvold suggests a Nested Loop Model for neural network development as depicted in Figure 8. This model combines two commonly used software development models: the waterfall model and some aspects of the spiral model. The model includes five steps that are briefly described below.

Neural networks can adapt in real time, allowing them to self-organize or structure themselves to a solution. Neural networks can derive meaning from complicated data sets (and thus may be Adaptive System and Neural Network Selection • • 3S able to provide a partial solution to NP-complete problems). Neural networks can overcome noisy data and still generate a correct answer. Neural networks are potentially fault tolerant so if a part of the network experiences a fault, the remaining part of the network may still operate correctly.

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