Forecast and Processing of Weak Electrical Signals in Clivia miniata by RBF Neural Networks

Jin Li Ding; Lan Zhou Wang

doi:10.4028/www.scientific.net/AMR.216.388

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 216Forecast and Processing of Weak Electrical Signals...

Forecast and Processing of Weak Electrical Signals in Clivia miniata by RBF Neural Networks

Abstract:

Original weak electrical signals in Clivia miniata were tested by a touching test system of self-made double shields with platinum sensors. Tested data of electrical signals denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting system was set up to forecast the signal in plants. Testing result shows that it is feasible to forecast the plant electrical signal for a short period. The forecast data can be used as an important preferences for the intelligent automatic control system based on the adaptive characteristic of plants to achieve the energy saving on agricultural production both the greenhouse and /or the plastic lookum.

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Periodical:

Advanced Materials Research (Volume 216)

Pages:

388-392

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.216.388

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Online since:

March 2011

Authors:

Jin Li Ding, Lan Zhou Wang

Keywords:

Clivia miniata, Intelligent Control, Processing of Weak Electric Signal, Radial Base Function (RBF) Neural Network, Wavelet Soft Threshold Denoising

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References

[1] L. Z. Wang, H. X. Li, M. Lin, et al.: Analysis of plant electrical signal in the time domain and frequency domain. Journal of China Jiliang University 16 (2005), pp.294-298.

Google Scholar

[2] C. H. Lou: The substance transportation and information transfer during the growth of higher plants (2) . Bulletin of Biology 12 (1991) pp.1-3.

Google Scholar

[3] H. Y. Ren, X. C. Wang, C. H. Lou: The universal existence of electrical signals and its physiological effects in higher plants. Acta Phytophysiologica Sinica 19 (1993) pp.97-101.

Google Scholar

[4] Z. Y. Wang, D. S. Chen, L. Huang: Plant physiological status monitoring system and its application in greenhouse. Transactions of the CSAE 16 (2000) pp.101-104.

Google Scholar

[5] Q. S. Guo, C. H. Su, C. R. Chen: Discussion of prediction earthquake mechanism of bioelectric potential of silk tree. Earthquake Research in Shanxi(supplement) (1999) pp.25-27.

Google Scholar

[6] J. L. Ding, G. Y. Ding, H. X. LI, et al.: Studies on the electrical signal of a seedling in Cucumis sativus L. Journal of Zhejiang Science and technology College 18 (2006) pp.180-184.

Google Scholar

[7] L. Z. Wang, H. X. Li, M. Lin, et al.: Application statistical analysis method in the study of the plant electrical signal. Journal of Jishou University 27 (2006) pp.67-70.

Google Scholar

[8] L. Z. Wang, W. X. Cao, L J. Ling: The determination of weak electrical signal in leaves of Lycoris radiata. Jornal of Northwest Normal University 36 (2000) pp.62-66.

Google Scholar

[9] L. Z. Wang, Q. Li, D. S. Li, et al.: Analysis of electrical signal in Osmanthus fragrans . The 6th Int. Sym. on Instrumentation and Control Technology, Proc. of SPIE 6357 (2006) p. 63570N-1-7.

Google Scholar

[10] Donoho D L. De-moise by soft-thresholding. IEEE Trans on IT 3 (1995) pp.613-327.

Google Scholar

[11] Nishida S. Automatic detection method of P300 waveform in single sweep records by using a neural network. Med Eng Phys 16 (1994) p.425.

DOI: 10.1016/1350-4533(90)90010-6

Google Scholar