Optimal Parameters for Multi-Pump Control System with Blind Source Separation Based on Independent Component Analysis

Abstract:

Finding the optimal parameters for multi-pump control system of water supply is regarded as a difficult problem with blind source separation (BSS). A motor acceleration or deceleration curve of water supply can be assumed as a weighted sum by feature control parameters, such as pipe line pressure, water flux, and sleeping interval, etc. With the help of destruction of separation model, a group of optimal parameters are found. The solution is to the optimal control parameters for multi-pump control system of water supply. The test results showed that separating precision is 0.856% for two main feature parameters. And an excellent agreement is found with the numerical simulation and the actual test parameters.

• Periodical: Applied Mechanics and Materials (Volumes 501-504)
• Pages: 2344-2347
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.501-504.2344
• Online since: January 2014
• Authors: Shu Fang Li*
• Keywords: Blind Source Separation, Dependent Component Analysis, Multi-Pump Control, System Parameters Optimization

References

[1] Lahat D., Cardoso J.F., Messer H., Second-Order Multidimensional ICA: Performance Analysis, IEEE Transactions on Signal Processing. Vol. 60, No. 9(2012), pp.4598-4610.

DOI: 10.1109/tsp.2012.2199985

[2] Anderson M., Adali T., Li X.L., Joint Blind Source Separation with Multivariate Gaussian Model: Algorithms and Performance Analysis, IEEE Transactions on Signal Processing. Vol. 60, No. 4(2012), pp.1672-1683.

DOI: 10.1109/tsp.2011.2181836

[3] Wang F., Li H., Li R., Harmonic Signals Retrieval Approach Based on Blind Source Separation, Circuits Syst Signal Process. Vol. 29(2010), pp.669-685.

DOI: 10.1007/s00034-010-9175-7

[4] Li R., Li H., Wang F., Dependent Component Analysis: Concepts and Main Algorithms, Journal of Computers. Vol. 5, No. 4(2010), pp.589-597.

[5] G. Mussardo.: Statistical Field Theory. An Introduction to Exactly Solved Models of Statistical Physics Oxford University Press, (2010).

DOI: 10.1093/oso/9780198788102.003.0001

[6] V. Zarzoso, P. Comon. Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size, IEEE Transactions on Neural Networks. Vol. 21, No. 2(2010), pp.248-261.

DOI: 10.1109/tnn.2009.2035920

[7] Langloris D, Chartier S, Gosselin D. An Introduction to Independent Componet Analysis: InforMax and FastICA Algorithms, Tutorials in Quantitative Methods for Psychology. Vol. 6, No. 1(2010), pp.31-38.

DOI: 10.20982/tqmp.06.1.p031

[8] Fasong Wang, Rui Li, BSS Algorithm by Diffusion Mixing Nonparametric Density Estimator, International Journal of Modelling, Identification and Control. Vol. 15, No. 1(2012), pp.13-19.

DOI: 10.1504/ijmic.2012.043936