Abstract
Given the relative limitations of BP and GA based leaning algorithms, Particle Swarm Optimization (PSO) is proposed to train Artificial Neural Networks (ANN) for the diagnosis of unexplained syncope. Compared with BP and GA based training techniques, PSO based learning method improves the diagnosis accuracy and speeds up the convergence process. Experimental results show that PSO is a robust training algorithm and should be extended to other real-world pattern classification applications.
This paper is supported by the National Basic Research Program of China (973 Program), No.2004CB719405 and the National Natural Science Foundation of China, No. 50305008.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hornik, K., Stinchcombe, M., White, H.: Multilayer Feed-forward Networks Are Universal Approximators. Neural Networks 2, 359–366 (1989)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back Propagating Errors. Nature 323, 533–536 (1986)
Sexton, R.S., Dorsey, R.E.: Reliable Classification Using Neural Networks: A Genetic Algorithm and Back Propagation Comparison. Decision Support Systems 30, 11–22 (2000)
Yang, J.M., Kao, C.Y.: A Robust Evolutionary Algorithm for Training Neural Networks. Neural Computing and Application 10, 214–230 (2001)
Franchini, M.: Use of A Genetic Algorithm Combined with A Local Search Method for the Automatic Calibration of Conceptual Rainfall-runoff Models. Hydrological Science Journal 41, 21–39 (1996)
Shi, Y.H., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. IEEE Congress on Evolutionary Computation (1999)
Salman, A., Ahmad, I.: Particle Swarm Optimization for Task Assignment Problem. Microprocessors and Microsystems 26, 363–371 (2002)
Yoshida, H., Kawata, K., Yoshikazu, F.: A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment. IEEE transaction on power system 15, 1232–1239 (2000)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufman, San Francisco (2001)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neutral Networks, Perth, Australia, pp. 1942–1948 (1995)
Shi, Y.H., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: The 7th Annual Conference on Evolutionary Programming, San Diego, USA (1998)
Shi, Y.H., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska (1998)
Yao, Y.J., Sun, X.Q., Wu, X.Y., Wu, Y.: Upright Tilt Table Testing and Syncope Evaluation. Space Medicine & Medical Engineering 15, 136–139 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, L., Zhou, C., Gao, HB., Shi, YR. (2006). Combining Particle Swarm Optimization and Neural Network for Diagnosis of Unexplained Syncope. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_19
Download citation
DOI: https://doi.org/10.1007/11816102_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
Online ISBN: 978-3-540-37282-0
eBook Packages: Computer ScienceComputer Science (R0)