(14th-February-2021)
• Learning from experience: Complex difficult to solve problems, but with plenty of data that describe the problem
• Generalizing from examples: Can interpolate from previous learning and give the correct response to unseen data
• Rapid applications development: NNs are generic machines and quite independent from domain knowledge
• Adaptability: Adapts to a changing environment, if is properly designed
• Computational efficiency: Although the training off a neural network demands a lot of computer power, a trained network demands almost nothing in recall mode
• Non-linearity: Not based on linear assumptions about the real word.
• Projects are data driven: Therefore, there is a need to collect and analyse data as part of the design process and to train the neural network. This task is often time-consuming and the effort, resources and time required are frequently underestimated
• It is not usually possible to specify fully the solution at the design stage: Therefore, it is necessary to build prototypes and experiment with them in order to resolve design issues. This iterative development process can be difficult to control
• Performance, rather than speed of processing, is the key issue: More attention must be paid to performance issues during the requirements analysis, design and test phases.
Furthermore, demonstrating that the performance meets the requirements can be particularly difficult.
• These issues affect the following areas :
• Project planning
• Project management
• Project documentation
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