A software development process model for artificial neural networks in critical applications
Abstract
Using artificial neural networks (ANNs) in critical applications can be challenging due to the often experimental nature of ANN construction and the "black box" label that is frequently attached to ANNs. Well-accepted process models exist for algorithmic software development which facilitate software validation and acceptance. The software development process model presented herein is targeted specifically toward ANNs in critical applications. This model is not unwieldy, and could easily be used on projects without critical aspects. This should be of particular interest to organizations that use ANNs and need to maintain or achieve a capability maturity model or ISO software development rating. Further, while this model is aimed directly at neural network development, with minor modifications, the model could be applied to any technique wherein knowledge is extracted from existing data, such as other numeric approaches or knowledge-based systems.
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Process-based safety arguments: designed specifically for NN used in safety-related applications; for reinforcement learning, e.g., [69].
“Process-based safety arguments: designed specifically for NN used in safety-related applications [66]; for reinforcement learning, e.g., [69].”
From A requirements model for AI algorithms in functional safety-critical systems with an explainable self-enforcing network from a developer perspective (2024) · cites this paper as [69]
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- Authors
- Rodvold, D. M.
- Affiliations
- Colorado Technical University (US)
- Corresponding author
- Rodvold, D.M.
- Venue
- IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)
- Publisher
- IEEE
- Published
- 2003-01-22
- Volume
- 5
- Pages
- 3317-3322
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W1888896835
- Cited by
- 34
- References
- 7
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 5 · 2018: 4 · 2017: 2 · 2015: 1 · 2012: 1
- FWCI
- 2.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- artificial neural networks, software development models, critical applications, software validation, software acceptance, capability maturity model, knowledge extraction, expert systems
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