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.)

"Applications of Neural Networks in excessive insurance structures" is the 1st booklet without delay addressing a key a part of neural community know-how: tools used to cross the cruel verification and validation (V&V) criteria required in lots of safety-critical purposes. The publication provides what varieties of review tools were constructed throughout many sectors, and the way to go the checks. a brand new adaptive constitution of V&V is constructed during this booklet, diverse from the easy six sigma equipment frequently used for large-scale platforms and diversified from the theorem-based method used for simplified part subsystems.

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Contract Research Report 327/2001, Liverpool John Moores University, Liverpool (2001) 32. : Adaptive Control Software: Can we guarantee safety? In: Proc. of the 28th International Computer Software and Applications Conference; Workshop on Software Cybernetics (2004) 33. : Predicting with Confidence—An Improved Dynamic Cell Structure. In: Advances in Neural Computation, vol. 1, pp. 750– 759. Springer, Heidelberg (2005) 34. : Validating A Neural Network-based Online Adaptive System. PhD thesis, West Virginia University, Morgantown (2005) 35.

Industrial use of safety-related artificial neural networks. Contract Research Report 327/2001, Liverpool John Moores University, Liverpool (2001) 32. : Adaptive Control Software: Can we guarantee safety? In: Proc. of the 28th International Computer Software and Applications Conference; Workshop on Software Cybernetics (2004) 33. : Predicting with Confidence—An Improved Dynamic Cell Structure. In: Advances in Neural Computation, vol. 1, pp. 750– 759. Springer, Heidelberg (2005) 34. : Validating A Neural Network-based Online Adaptive System.

This chapter gives a general introduction on neural network complexity analysis. Different pruning algorithms for multi-layer feedforward neural networks are studied and computer simulation results are presented. 1 Introduction There are two fundamental issues in neuro-computation: learning algorithm development and the network topology design. In fact, these two issues are closely related with each other. The learning ability of a neural network is not only a function of time (or training iterations), but also a function of the network structure.

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