Abstract
Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis.
This project was supported by National 863 High-Tech, R&D Program for CIMS, China (Grant No. 2003AA414210) and Shenyang Science and Technology Program (Grant No. 1053084-2-02).
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References
Filippetti, F., Franceschini, G., Tassoni, C., Vas, P.: Recent Developments of Induction Motor Drives Fault Diagnosis using A1 Techniques. IEEE Trans. Industrial Electronics 47, 994–1003 (2000)
Eberhart, R.C., Kennedy, J.: A New Optimizer using Particle Swarm Theory. In: Proc. 6th Int. Symp on Micro Machine Human Science, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: PSO optimization. In: IEEE Int. Conf. Neural Networks. Perth, Australia, vol. 4, pp. 1941–1948 (1995)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm 0ptimizer. In: IEEE Int. Conf. Evolutionary Compulation, Anchorage, Alaska, vol. 5, pp. 69–73 (1998)
Fu, G.J., Wang, S.M., Liu, S.Y., Li, N.: An Improved Velocity Mutation Particle Swarm Optimizer. Computer Engineering and Application 13, 48–50 (2006)
Mueleod, J.D., Taylor, V., Laflamme, J.C.G.: Implanted Component Faults and Their Effects on Gas Turbine Engine Performance. Engineering for Gas Turbine and Power 114, 174–179 (1992)
Diakunchak, I.S.: Performance Deterioration in Industrial Gas Turbines. Engineering for Gas Turbine and Power 114, 161–168 (1992)
Weng, S.L., Wang, Y.H.: Intelligent Fault Diagnosis of Gas Turbine Based on Thermal Parameters. Journal of Shanghai Jiaotong University 36, 165–168 (2002)
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Hu, W., Hu, J. (2007). A New BP Network Based on Improved PSO Algorithm and Its Application on Fault Diagnosis of Gas Turbine. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_36
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DOI: https://doi.org/10.1007/978-3-540-72395-0_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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