Abstract
Neural network the number of hidden neurons for the network performance has a significant impact, usually for a specific problem, there is no way to determine a certain level in the end should be hidden together the number of neurons, the general test Way through many experiments to achieve the desired effect. The improved BP algorithm, the establishment of the BP neural network diagnostic model, tested its correct diagnosis was 100%, BP model diagnostic accuracy was 95.39%. The results show that the BP neural network suitable for solving the complex problem of disease diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bertoldo, A., Peltoniemi, P., Oikonen, V., et al.: Kinetic modeling of [18F] FDG in skeletal muscle by PET:a four-compartment five-rate-constant model. American Journal of Physiology-Endocrinology and Metabolism 28(3), 524–536 (2001)
Price, P.: PET as a potential tool for imaging molecular mechanisms of oncology in man. Trends in Molecular Medicine 7(10), 442–446 (2001)
Medicine clinics, the proposed automatic pulse. Pulse mathematical analysis and design concept in Chinese medicine research 3, 15–16 (1995)
Zhang, W.A., Fen, W.T., et al.: Pulse of the Computer Identification and Classification of Biomedical Engineering Journal 9(1), 86–90 (1992)
Gamma, E.: Design Pattern: Elements of Reusable Object Oriented Software. Addison Wesley Longman, Inc., Amsterdam (1995)
Gallentti, G.G., Venegas, J.G.: Tracer kinetic model of regional pulmonary function using positron emission tomography. J. Appl. Phys. 93(3), 1104–1114 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiong, W., Du, J., Shu, Q., Zhao, Y. (2011). Artificial Neural Network Based Modeling of Glucose Metabolism. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23756-0_100
Download citation
DOI: https://doi.org/10.1007/978-3-642-23756-0_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23755-3
Online ISBN: 978-3-642-23756-0
eBook Packages: EngineeringEngineering (R0)