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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Extra resources for Applications of Neural Networks in High Assurance Systems
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.