Abstract
We have applied intelligent control theory into medical psychiatric diagnosis. During studying, we find selecting suitable neural network structure is very important to BP network. Suitable neural network structure will bring less error to diagnosis system. It is key to success or failure of psychiatric intelligent diagnosis system. We have found some rules that suit our diagnosis system. Such as : full connecting mode is better and at the same time adding suitable hidden node number can improve convergence effect and reduce error of network. But adding hidden layer number doesn’t always improve network convergence effect under our studying. At the same time it makes network convergence speed to become slower and makes network training time to increase. We build an intelligent diagnosis system. Comparing the diagnosis by computer with the senior child psychiatrists, the consistent rate of intelligent diagnosis is 99%.
Foundation item: Project (39270262) supported by the National Natural Science Foundation of China, The research wins the third prize of Hunan province medical science and technology progress.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hua, J.: Application of fuzzy neural network in multi-maneuvering target tracking. In: 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR), pp. 92–95 (2010)
Chen, K., Zeniewski, J., Zhao, P., Chen, R.E.Y., et al.: Aggregate Volumetric Estimation Based on PCA and Momentum-enhanced BP Neural Network 643 Image based sieving for particle aggregate gradation. Journal of Electronics 25(2), 277–282 (2008)
Kapoor, R., Pal, D., Chakravartty, J.K.: Use of artificial neural networks to predict the deformation behavior of Zr-2.5Nb-0.5Cu. Journal of Materials Processing Technology 169, 99–205 (2005)
Ge, S.S., Hong, F., Lee, T.: Adaptive Neural Control of Nonlinear Time2 Delay Systems with Unknown Virtual Control Co2 efficients. IEEE Transaction on Systems, Man, and Cybernetics Part B: Cybernetics 34(1), 499–516 (2004)
Chen, M., Jiang, C., Wu, Q.: Back stepping Control for a Class of Uncertain Nonlinear Systems with Neural Network. International Journal of Nonlinear Science 3(2), 137–143 (2007)
Min, X.L., Liu, G.H.: BP network application development using MATLAB neural network toolbox. Computer Applications 21(8), 163–164 (2008)
Ni, Z.W., Ye, H.Y., Cao, H.H.: Efficient sequential pattern mining algorithm based on average value constraint satisfaction pruning strategy. Journal of University of Science and Technology of China (2), 217–220 (2007)
Su, J., Li, H., Dong, Q., Liu, P.: Modelling of Rolling and Aging Processes in Copper Alloy by Levenberg-Marquardt BP Algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 185–189. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Chen, B.M., Fan, X.P., Zhou, Z.M., Li, X.R. (2012). Neural Network and Artificial Intelligence Study in Psychiatric Intelligent Diagnosis. In: Zhang, T. (eds) Mechanical Engineering and Technology. Advances in Intelligent and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27329-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-27329-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27328-5
Online ISBN: 978-3-642-27329-2
eBook Packages: EngineeringEngineering (R0)