Titlebook: Methods and Procedures for the Verification and Validation of Artificial Neural Networks; Brian J. Taylor Book 2006 Springer-Verlag US 200

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Neural Network Verification,arding major verification issues that are peculiar to neural network based systems..Four general areas have been addressed:.The references are selected to provide the reader with a comprehensive set of survey references that are readily assessable to those with limited knowledge of neural network technology.

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Augmentation of Current Verification and Validation Practices,sfy these gaps. Through the mapping of software V&V standards, process guidance, previous research, and IFCS documentation, a guidance document was formulated to augment current V&V practices to accommodate the characteristics of neural networks or adaptive systems. The guidance document [ISR 2005]

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Validation of Neural Networks Via Taxonomic Evaluation,ation involves the assessment of the appropriateness of a particular neural network-based solution for the problem being solved.This chapter has presented a taxonomic overview of neural network systems from multiple perspectives. Major characteristics of applications and their solution via neural ne

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Stability Properties of Neural Networks,punov functions can be described which then provide a rigorous characterization of network behavior across all of the possible set of states for the network for which the Lyapunov function is defined. In combination with probabilistic methods such as those of Schumann and Gupta [Schumann 2003], a hi

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