| Autor: |
Partridge, Derek, Sharkey, Noel E. |
| Zdroj: |
Expert Systems; Aug1994, Vol. 11 Issue 3, p167-176, 10p |
| Abstrakt: |
We present the results of a feasibility study for the application of neural computing to the traditional problem of how to generate cost-effective, reliable implementations of complex problems-i.e. the central problem of software engineering. We treat neural computing as an innovative technology for conventional software engineering. We explore the reliability of neural networks (multilayer perceptrons trained with the backpropagation algorithm) as alternative versions in a multiversion software system. The basic idea is that versions trained differently will not exhibit common faults as independently developed, conventional versions (programmed in, for example, Modula-2) have been shown to do. The common design faults that run through independently developed versions appear to be the result of 'difficult' inputs which all programmers tend to misconstrue similarly. Network implementations, which are not directly designed in the conventional manner, should permit easy introduction of 'diversity' to combat this weakness. The initial results give credence to this possibility and have shown the way to generate substantial forced diversity within the neural computing paradigm. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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