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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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
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