Applications of Neural Networks in High Assurance Systems by Johann Schumann, Pramod Gupta, Yan Liu (auth.), Johann PDF

By Johann Schumann, Pramod Gupta, Yan Liu (auth.), Johann Schumann, Yan Liu (eds.)

ISBN-10: 3642106897

ISBN-13: 9783642106897

ISBN-10: 3642106900

ISBN-13: 9783642106903

"Applications of Neural Networks in excessive coverage structures" is the 1st booklet without delay addressing a key a part of neural community know-how: tools used to cross the harsh verification and validation (V&V) criteria required in lots of safety-critical purposes. The e-book provides what varieties of overview equipment were built throughout many sectors, and the way to go the exams. a brand new adaptive constitution of V&V is constructed during this ebook, varied from the straightforward six sigma equipment frequently used for large-scale platforms and assorted from the theorem-based technique used for simplified part subsystems.

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Unlike the algorithms discussed in (10; 13), this algorithm can be applied to NN with multiple hidden layers. However, as we discussed earlier, relying on one single value of σ02 for the entire network can lead to problems. Fnaiech et. al. (1) suggested that parameters within the same layer should be considered “locally” rather than “globally”, and defined a new pruning index called the local parameter variance nullity (LPVN). The PVN for all parameters in the same layer are summed up; then the LPVN for each parameter (which represents the relative importance of PVN of a parameter in the layer) can be obtained and used for pruning: γθ[l] (20) Lγθ[l] = K k k γθ[l] k=1 k where Lγθ[l] is the LPVN for layer l, and K is the total number of parameters k in layer l.

The final two chapters discuss how neural networks can improve the efficiency of processes (blending of crude oil) and fuel cells. We hope that the wide range of applications and methods described in the book illustrate the potential of neural networks in safety-critical and highassurance applications and help the reader to be more aware of issues and approaches and to drive the advances of V&V of such systems to ultimately make them safe and reliable. References 1. American Power Conference. Proceesings of the American Power Conference 1998, vol.

A foundation for neural network verification and validation. In: SPIE Science of Artificial Neural Networks II, vol. 1966, pp. 196–207 (1993) 42. : Software Engineering: A Practitioner’s Approach. McGraw-Hill, New York (1999) Application of Neural Networks in High Assurance Systems: A Survey 19 43. : Toyota Prius HEV Neurocontrol and Diagnostics. Neural Networks 21(2-3), 458–465 (2008) 44. : Guidance for the Verification and Validation of Neural Networks. Emerging Technologies. Wiley-IEEE Computer Society Press (2007) 45.

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Applications of Neural Networks in High Assurance Systems by Johann Schumann, Pramod Gupta, Yan Liu (auth.), Johann Schumann, Yan Liu (eds.)

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